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transformers | transformers-main/tests/models/focalnet/test_modeling_focalnet.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch FocalNet model. """
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class FocalNetModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
embed_dim=16,
hidden_sizes=[32, 64, 128],
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=True,
type_sequence_label_size=10,
encoder_stride=8,
out_features=["stage1", "stage2"],
out_indices=[1, 2],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.hidden_sizes = hidden_sizes
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.type_sequence_label_size = type_sequence_label_size
self.encoder_stride = encoder_stride
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return FocalNetConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
encoder_stride=self.encoder_stride,
out_features=self.out_features,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels):
model = FocalNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))
def create_and_check_backbone(self, config, pixel_values, labels):
model = FocalNetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.image_size, 8, 8])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[:-1])
# verify backbone works with out_features=None
config.out_features = None
model = FocalNetBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.image_size * 2, 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels):
model = FocalNetForMaskedImageModeling(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)
)
# test greyscale images
config.num_channels = 1
model = FocalNetForMaskedImageModeling(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = FocalNetForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
config.num_channels = 1
model = FocalNetForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class FocalNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = FocalNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=FocalNetConfig, embed_dim=37, has_text_modality=False)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_for_masked_image_modeling(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@unittest.skip(reason="FocalNet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# FocalNet has a different seq_length
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
reshaped_hidden_states = outputs.reshaped_hidden_states
self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
reshaped_hidden_states = (
reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
def test_hidden_states_output_with_padding(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.patch_size = 3
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
@slow
def test_model_from_pretrained(self):
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = FocalNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@require_vision
@require_torch
class FocalNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny").to(torch_device)
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([0.2166, -0.4368, 0.2191]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
self.assertTrue(outputs.logits.argmax(dim=-1).item(), 281)
@require_torch
class FocalNetBackboneTest(BackboneTesterMixin, unittest.TestCase):
all_model_classes = (FocalNetBackbone,) if is_torch_available() else ()
config_class = FocalNetConfig
has_attentions = False
def setUp(self):
self.model_tester = FocalNetModelTester(self)
| 17,984 | 38.183007 | 117 | py |
transformers | transformers-main/tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Wav2Vec2-Conformer model. """
import math
import unittest
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2ConformerConfig, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
Wav2Vec2ConformerForAudioFrameClassification,
Wav2Vec2ConformerForCTC,
Wav2Vec2ConformerForPreTraining,
Wav2Vec2ConformerForSequenceClassification,
Wav2Vec2ConformerForXVector,
Wav2Vec2ConformerModel,
Wav2Vec2FeatureExtractor,
Wav2Vec2Processor,
)
from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import (
Wav2Vec2ConformerGumbelVectorQuantizer,
_compute_mask_indices,
_sample_negative_indices,
)
class Wav2Vec2ConformerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=16,
feat_extract_norm="group",
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=4,
num_attention_heads=2,
hidden_dropout_prob=0.1,
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
mask_time_prob=0.5,
mask_time_length=2,
vocab_size=32,
do_stable_layer_norm=False,
num_adapter_layers=1,
adapter_stride=2,
tdnn_dim=(32, 32),
tdnn_kernel=(5, 3),
tdnn_dilation=(1, 2),
xvector_output_dim=32,
position_embeddings_type="relative",
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.num_adapter_layers = num_adapter_layers
self.adapter_stride = adapter_stride
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.scope = scope
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
self.position_embeddings_type = position_embeddings_type
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self, position_embeddings_type="relative"):
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config(position_embeddings_type=position_embeddings_type)
return config, input_values, attention_mask
def get_config(self, position_embeddings_type="relative"):
return Wav2Vec2ConformerConfig(
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
mask_time_prob=self.mask_time_prob,
mask_time_length=self.mask_time_length,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
do_stable_layer_norm=self.do_stable_layer_norm,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
num_adapter_layers=self.num_adapter_layers,
adapter_stride=self.adapter_stride,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
position_embeddings_type=position_embeddings_type,
)
def create_and_check_model(self, config, input_values, attention_mask):
model = Wav2Vec2ConformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter(self, config, input_values, attention_mask):
config.add_adapter = True
model = Wav2Vec2ConformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 2 * config.hidden_size
model = Wav2Vec2ConformerForCTC(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size)
)
def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 8
model = Wav2Vec2ConformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = Wav2Vec2ConformerModel(config=config)
model.to(torch_device)
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = Wav2Vec2ConformerForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_values, *args):
model = Wav2Vec2ConformerForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ConformerForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ConformerForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_xvector_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ConformerForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_values, *args):
model = Wav2Vec2ConformerForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with self.parent.assertRaises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class Wav2Vec2ConformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
Wav2Vec2ConformerForCTC,
Wav2Vec2ConformerModel,
Wav2Vec2ConformerForSequenceClassification,
Wav2Vec2ConformerForPreTraining,
Wav2Vec2ConformerForAudioFrameClassification,
Wav2Vec2ConformerForXVector,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"audio-classification": Wav2Vec2ConformerForSequenceClassification,
"automatic-speech-recognition": Wav2Vec2ConformerForCTC,
"feature-extraction": Wav2Vec2ConformerModel,
}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
test_torchscript = False
def setUp(self):
self.model_tester = Wav2Vec2ConformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2ConformerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_relative(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="relative")
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_rotary(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type="rotary")
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_no_rel_pos(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs(position_embeddings_type=None)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_for_ctc(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Wav2Vec2Conformer has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Wav2Vec2Conformer cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Wav2Vec2Conformer has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_flax_to_pt(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_pt_to_flax(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"pos_bias_v",
"pos_bias_u",
"pointwise_conv1",
"pointwise_conv2",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None:
module.pos_bias_u.data.fill_(3)
if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None:
module.pos_bias_v.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
def test_mask_feature_prob_ctc(self):
model = Wav2Vec2ConformerForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2-conformer", mask_feature_prob=0.2, mask_feature_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_prob_ctc(self):
model = Wav2Vec2ConformerForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2-conformer", mask_time_prob=0.2, mask_time_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2-conformer", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
model = Wav2Vec2ConformerModel.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
self.assertIsNotNone(model)
@require_torch
class Wav2Vec2ConformerUtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_low_prob(self):
# with these settings num_masked_spans=0.5, which means probabilistic rounding
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
# the other 5 out of 10, cases num_masked_spans=1
n_trials = 100
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
count_dimensions_masked = 0
count_dimensions_not_masked = 0
for _ in range(n_trials):
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
num_masks = torch.sum(mask).item()
if num_masks > 0:
count_dimensions_masked += 1
else:
count_dimensions_not_masked += 1
# as we test for at least 10 masked dimension and at least
# 10 non-masked dimension, this test could fail with probability:
# P(100 coin flips, at most 9 heads) = 1.66e-18
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
mask = torch.from_numpy(mask).to(torch_device)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_mask_indices_short_audio(self):
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
# force one example to be heavily padded
attention_mask[0, 5:] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
)
# make sure that non-padded examples cannot be padded
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
def test_compute_perplexity(self):
probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100
ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs)
self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3)
# mask half of the input
mask = torch.ones((2,), device=torch_device, dtype=torch.bool)
mask[0] = 0
ppl = Wav2Vec2ConformerGumbelVectorQuantizer._compute_perplexity(probs, mask)
self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3)
def test_sample_negatives(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# sample negative indices
sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
def test_sample_negatives_with_mask(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
# second half of last input tensor is padded
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
mask[-1, sequence_length // 2 :] = 0
features = (torch.arange(sequence_length * hidden_size, device=torch_device) // hidden_size).view(
sequence_length, hidden_size
) # each value in vector consits of same value
features = features[None, :].expand(batch_size, sequence_length, hidden_size).contiguous()
# replace masked feature vectors with -100 to test that those are not sampled
features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100)
# sample negative indices
sampled_negative_indices = _sample_negative_indices(
(batch_size, sequence_length), num_negatives, mask.cpu().numpy()
)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue((negatives >= 0).all().item())
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertTrue(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
@require_torch
@slow
class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)])
speech_samples = speech_samples[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_inference_ctc_normal_batched_rel_pos(self):
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large-960h-ft")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained(
"facebook/wav2vec2-conformer-rel-pos-large-960h-ft", do_lower_case=True
)
input_speech = self._load_datasamples(2)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loincloth that was the only garment he wore",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_normal_batched_rope(self):
model = Wav2Vec2ConformerForCTC.from_pretrained("facebook/wav2vec2-conformer-rope-large-960h-ft")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained(
"facebook/wav2vec2-conformer-rope-large-960h-ft", do_lower_case=True
)
input_speech = self._load_datasamples(2)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_pretrained(self):
model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
model.to(torch_device)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"facebook/wav2vec2-conformer-rel-pos-large", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
batch_size = inputs_dict["input_values"].shape[0]
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
features_shape = (batch_size, feature_seq_length)
torch.manual_seed(0)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
with torch.no_grad():
outputs = model(
inputs_dict.input_values.to(torch_device),
attention_mask=inputs_dict.attention_mask.to(torch_device),
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)
# retrieve cosine sim of masked features
cosine_sim_masked = cosine_sim[mask_time_indices]
# ... now compare to randomly initialized model
config = Wav2Vec2ConformerConfig.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
model_rand = Wav2Vec2ConformerForPreTraining(config).to(torch_device).eval()
with torch.no_grad():
outputs_rand = model_rand(
inputs_dict.input_values.to(torch_device),
attention_mask=inputs_dict.attention_mask.to(torch_device),
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim_rand = torch.cosine_similarity(
outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1
)
# retrieve cosine sim of masked features
cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices]
# a pretrained wav2vec2_conformer model has learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states > 0.5
# a random wav2vec2_conformer model has not learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states is very likely < 0.1
self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)
| 39,275 | 40.343158 | 149 | py |
transformers | transformers-main/tests/models/rembert/test_modeling_rembert.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch RemBERT model. """
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RemBertConfig,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertModel,
)
from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class RemBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
input_embedding_size=18,
output_embedding_size=43,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.input_embedding_size = input_embedding_size
self.output_embedding_size = output_embedding_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = RemBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
input_embedding_size=self.input_embedding_size,
output_embedding_size=self.output_embedding_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RemBertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RemBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = RemBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RemBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RemBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RemBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = RemBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
RemBertModel,
RemBertForMaskedLM,
RemBertForCausalLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (RemBertForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": RemBertModel,
"fill-mask": RemBertForMaskedLM,
"question-answering": RemBertForQuestionAnswering,
"text-classification": RemBertForSequenceClassification,
"text-generation": RemBertForCausalLM,
"token-classification": RemBertForTokenClassification,
"zero-shot": RemBertForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = RemBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
@slow
def test_model_from_pretrained(self):
for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = RemBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class RemBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_model(self):
# Test exact values at the last hidden layer
model = RemBertModel.from_pretrained("google/rembert")
input_ids = torch.tensor([[312, 56498, 313, 2125, 313]])
segment_ids = torch.tensor([[0, 0, 0, 1, 1]])
with torch.no_grad():
output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True)
hidden_size = 1152
expected_shape = torch.Size((1, 5, hidden_size))
self.assertEqual(output["last_hidden_state"].shape, expected_shape)
expected_implementation = torch.tensor(
[
[
[0.0754, -0.2022, 0.1904],
[-0.3354, -0.3692, -0.4791],
[-0.2314, -0.6729, -0.0749],
[-0.0396, -0.3105, -0.4234],
[-0.1571, -0.0525, 0.5353],
]
]
)
# Running on the original tf implementation gives slightly different results here.
# Not clear why this variations is present
# TODO: Find reason for discrepancy
# expected_original_implementation = [[
# [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
# [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
# [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
# [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
# [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
# ]]
self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4))
| 20,093 | 38.169591 | 117 | py |
transformers | transformers-main/tests/models/whisper/test_feature_extraction_whisper.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_torchaudio
class WhisperFeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=10,
hop_length=160,
chunk_length=8,
padding_value=0.0,
sampling_rate=4_000,
return_attention_mask=False,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
self.feature_size = feature_size
self.chunk_length = chunk_length
self.hop_length = hop_length
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
else:
# make sure that inputs increase in size
speech_inputs = [
floats_list((x, self.feature_size))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class WhisperFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = WhisperFeatureExtractor if is_speech_available() else None
def setUp(self):
self.feat_extract_tester = WhisperFeatureExtractionTester(self)
def test_feat_extract_from_and_save_pretrained(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
check_json_file_has_correct_format(saved_file)
feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_feat_extract_to_json_file(self):
feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "feat_extract.json")
feat_extract_first.to_json_file(json_file_path)
feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
dict_first = feat_extract_first.to_dict()
dict_second = feat_extract_second.to_dict()
mel_1 = feat_extract_first.mel_filters
mel_2 = feat_extract_second.mel_filters
self.assertTrue(np.allclose(mel_1, mel_2))
self.assertEqual(dict_first, dict_second)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test feature size
input_features = feature_extractor(np_speech_inputs, padding="max_length", return_tensors="np").input_features
self.assertTrue(input_features.ndim == 3)
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)
# Test not batched input
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test truncation required
speech_inputs = [floats_list((1, x))[0] for x in range(200, (feature_extractor.n_samples + 500), 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
speech_inputs_truncated = [x[: feature_extractor.n_samples] for x in speech_inputs]
np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated]
encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_features.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_integration(self):
# fmt: off
EXPECTED_INPUT_FEATURES = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
]
)
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
self.assertEqual(input_features.shape, (1, 80, 3000))
self.assertTrue(torch.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
audio = self._load_datasamples(1)[0]
audio = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
audio = feat_extract.zero_mean_unit_var_norm([audio], attention_mask=None)[0]
self.assertTrue(np.all(np.mean(audio) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(audio) - 1) < 1e-3))
| 10,672 | 43.103306 | 118 | py |
transformers | transformers-main/tests/models/whisper/test_modeling_flax_whisper.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import inspect
import tempfile
import unittest
import transformers
from transformers import WhisperConfig, is_flax_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
from transformers.utils import cached_property
from transformers.utils.import_utils import is_datasets_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_datasets_available():
import datasets
from datasets import load_dataset
if is_flax_available():
import jax
import numpy as np
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import (
FLAX_MODEL_MAPPING,
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
WhisperFeatureExtractor,
WhisperProcessor,
)
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
@require_flax
class FlaxWhisperModelTester:
config_cls = WhisperConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=60,
is_training=True,
use_labels=False,
vocab_size=99,
d_model=16,
decoder_attention_heads=4,
decoder_ffn_dim=16,
decoder_layers=2,
encoder_attention_heads=4,
encoder_ffn_dim=16,
encoder_layers=2,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=70,
max_source_positions=30,
max_target_positions=40,
bos_token_id=98,
eos_token_id=98,
pad_token_id=0,
num_mel_bins=80,
decoder_start_token_id=85,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = encoder_layers
self.num_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = encoder_layers
self.encoder_seq_length = seq_length // 2
self.decoder_seq_length = 1
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
def prepare_config_and_inputs_for_common(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size)
decoder_input_ids = np.array(self.batch_size * [[self.decoder_start_token_id]])
config = WhisperConfig(
vocab_size=self.vocab_size,
num_mel_bins=self.num_mel_bins,
decoder_start_token_id=self.decoder_start_token_id,
is_encoder_decoder=True,
activation_function=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
pad_token_id=self.pad_token_id,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
tie_word_embeddings=True,
d_model=self.d_model,
decoder_attention_heads=self.decoder_attention_heads,
decoder_ffn_dim=self.decoder_ffn_dim,
decoder_layers=self.decoder_layers,
encoder_attention_heads=self.encoder_attention_heads,
encoder_ffn_dim=self.encoder_ffn_dim,
encoder_layers=self.encoder_layers,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
)
inputs_dict = prepare_whisper_inputs_dict(config, input_features, decoder_input_ids)
return config, inputs_dict
def prepare_whisper_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.int8),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id).astype(np.int8),
],
axis=-1,
)
return {
"input_features": input_ids,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
def partialclass(cls, *args, **kwargs):
class NewCls(cls):
__init__ = functools.partialmethod(cls.__init__, *args, **kwargs)
return NewCls
def make_partial_class(full_class, *args, **kwargs):
partial_class = partialclass(full_class, *args, **kwargs)
partial_class.__name__ = full_class.__name__
partial_class.__module__ = full_class.__module__
return partial_class
@require_flax
class FlaxWhisperModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxWhisperForConditionalGeneration, FlaxWhisperModel) if is_flax_available() else ()
all_generative_model_classes = (FlaxWhisperForConditionalGeneration,) if is_flax_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = FlaxWhisperModelTester(self)
_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.init_shape = (1,) + inputs_dict["input_features"].shape[1:]
self.all_model_classes = (
make_partial_class(model_class, input_shape=self.init_shape) for model_class in self.all_model_classes
)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
def test_config(self):
self.config_tester.run_common_tests()
# overwrite because of `input_features`
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "decoder_input_ids"]
self.assertListEqual(arg_names[:2], expected_arg_names)
# overwrite because of `input_features`
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_features, decoder_input_ids, **kwargs):
return model(input_features=input_features, decoder_input_ids=decoder_input_ids, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
model.params = model.to_bf16(model.params)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
# save pt model
pt_model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# convert Flax model to PyTorch model
pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
pt_model = pt_model_class(config).eval()
pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params)
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
def test_save_load_from_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = base_class(config)
base_params = flatten_dict(unfreeze(model.params))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
head_model = model_class.from_pretrained(tmpdirname)
base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
for key in base_param_from_head.keys():
max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
# overwrite because of `input_features`
def test_save_load_to_base(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
base_class = make_partial_class(FLAX_MODEL_MAPPING[config.__class__], input_shape=self.init_shape)
for model_class in self.all_model_classes:
if model_class.__name__ == base_class.__name__:
continue
model = model_class(config)
base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
# check that all base model weights are loaded correctly
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
base_model = base_class.from_pretrained(tmpdirname)
base_params = flatten_dict(unfreeze(base_model.params))
for key in base_params_from_head.keys():
max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
@slow
@require_flax
class FlaxWhisperModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_tiny_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-tiny", from_pt=True)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="np").input_features
logits = model(
input_features,
decoder_input_ids=np.array([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
# fmt: off
EXPECTED_LOGITS = np.array(
[
2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407,
0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246,
4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713,
0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0][0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_small_en_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-small.en", from_pt=True)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="np").input_features
logits = model(
input_features,
decoder_input_ids=np.array([model.config.decoder_start_token_id]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
logits = logits[0] @ model.params["model"]["decoder"]["embed_tokens"]["embedding"].T
# fmt: off
EXPECTED_LOGITS = np.array(
[
-3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188,
-8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935,
-6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781,
-10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509,
-11.1146, -8.1918
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_large_logits_librispeech(self):
model = FlaxWhisperModel.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(1)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
processed_inputs = processor(
audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="np"
)
input_features = processed_inputs.input_features
decoder_input_ids = processed_inputs.labels
logits = model(
input_features,
decoder_input_ids=decoder_input_ids,
output_hidden_states=False,
output_attentions=False,
return_dict=False,
)
logits = logits[0] @ model.params["model"]["decoder"]["embed_tokens"]["embedding"].T
# fmt: off
EXPECTED_LOGITS = np.array(
[
2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472,
1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357,
1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376,
1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184
]
)
# fmt: on
self.assertTrue(np.allclose(logits[0, 0, :30], EXPECTED_LOGITS, atol=1e-4))
def test_tiny_en_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
model.config.decoder_start_token_id = 50257
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad to"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_tiny_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny", from_pt=True)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(
raw_speech=input_speech, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="jax"
).input_features
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
generated_ids = model.generate(input_features, num_beams=5, max_length=20).sequences
transcript = processor.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_generation_multilingual(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
ds = load_dataset("common_voice", "ja", split="test", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
input_speech = next(iter(ds))["audio"]["array"]
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np")
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")
generated_ids = model.generate(input_features, do_sample=False, max_length=20).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="en", task="transcribe")
generated_ids = model.generate(
input_features,
do_sample=False,
max_length=20,
).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Kimura-san called me."
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="translate")
generated_ids = model.generate(input_features, do_sample=False, max_length=20).sequences
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
def test_large_batched_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-large", from_pt=True)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np").input_features
generated_ids = model.generate(input_features, max_length=20).sequences
# fmt: off
EXPECTED_LOGITS = np.array(
[
[50258, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404, 281],
[50258, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257, 50257],
[50258, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256],
[50258, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439, 11]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all,",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
def test_tiny_en_batched_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="np").input_features
generated_ids = model.generate(input_features, max_length=20).sequences
# fmt: off
EXPECTED_LOGITS = np.array(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_timestamp_generation(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
input_speech = np.concatenate(self._load_datasamples(4))
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="jax").input_features
generate_fn = jax.jit(functools.partial(model.generate, max_length=448, return_timestamps=True))
generated_ids = generate_fn(input_features)
# fmt: off
EXPECTED_OUTPUT = np.array([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257])
# fmt: on
self.assertTrue(np.allclose(generated_ids, EXPECTED_OUTPUT))
EXPECTED_TRANSCRIPT = [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is"
" Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season"
" of the year, with Christmas and roast beef looming before us, similarly drawn from eating and"
" its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins'"
" work is really Greek after all, and"
),
"offsets": [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
),
"timestamp": (0.0, 6.5600000000000005),
},
{
"text": " Nor is Mr. Quilter's manner less interesting than his matter.",
"timestamp": (6.5600000000000005, 11.24),
},
{
"text": (
" He tells us that at this festive season of the year, with Christmas and roast beef"
" looming"
),
"timestamp": (11.24, 16.88),
},
{
"text": (
" before us, similarly drawn from eating and its results occur most readily to the mind."
),
"timestamp": (16.88, 23.76),
},
{
"text": (
" He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and"
),
"timestamp": (23.76, 29.44),
},
],
}
]
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
class FlaxWhisperEncoderModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=60,
is_training=True,
use_labels=True,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
num_mel_bins=80,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
classifier_proj_size=4,
num_labels=2,
is_encoder_decoder=False,
is_decoder=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
self.classifier_proj_size = classifier_proj_size
self.num_labels = num_labels
self.is_encoder_decoder = is_encoder_decoder
self.is_decoder = is_decoder
def get_config(self):
return WhisperConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
classifier_proj_size=self.classifier_proj_size,
num_labels=self.num_labels,
is_encoder_decoder=self.is_encoder_decoder,
is_decoder=self.is_decoder,
)
def prepare_whisper_encoder_inputs_dict(
self,
input_features,
):
return {
"input_features": input_features,
}
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length])
config = self.get_config()
inputs_dict = self.prepare_whisper_encoder_inputs_dict(
input_features=input_features,
)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
@property
def encoder_seq_length(self):
return self.get_subsampled_output_lengths(self.seq_length)
@require_flax
class WhisperEncoderModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (FlaxWhisperForAudioClassification,) if is_flax_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = FlaxWhisperEncoderModelTester(self)
_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self.init_shape = (1,) + inputs_dict["input_features"].shape[1:]
self.all_model_classes = (
make_partial_class(model_class, input_shape=self.init_shape) for model_class in self.all_model_classes
)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
def test_config(self):
self.config_tester.run_common_tests()
# overwrite because of `input_features`
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_features, **kwargs):
return model(input_features=input_features, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite because of `input_features`
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "attention_mask", "output_attentions"]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_inputs_embeds(self):
pass
# WhisperEncoder has no inputs_embeds and thus the `get_input_embeddings` fn is not implemented
def test_model_common_attributes(self):
pass
# WhisperEncoder cannot resize token embeddings since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# WhisperEncoder does not have any base model
def test_save_load_to_base(self):
pass
# WhisperEncoder does not have any base model
def test_save_load_from_base(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_from_base_pt(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_to_base_pt(self):
pass
# WhisperEncoder does not have any base model
@is_pt_flax_cross_test
def test_save_load_bf16_to_base_pt(self):
pass
| 39,590 | 42.411184 | 636 | py |
transformers | transformers-main/tests/models/whisper/test_modeling_whisper.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Whisper model. """
import copy
import inspect
import os
import tempfile
import unittest
import numpy as np
import transformers
from transformers import WhisperConfig
from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_flax_available, is_torch_available
from transformers.utils.import_utils import is_datasets_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_datasets_available():
import datasets
from datasets import load_dataset
if is_torch_available():
import torch
from transformers import (
WhisperFeatureExtractor,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperProcessor,
set_seed,
)
from transformers.models.whisper.modeling_whisper import WhisperDecoder, WhisperEncoder
if is_flax_available():
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
def prepare_whisper_inputs_dict(
config,
input_features,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
# "input_ids": input_features,
"input_features": input_features,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_torch
class WhisperModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=60,
is_training=True,
use_labels=False,
vocab_size=200,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
max_target_positions=40,
bos_token_id=98,
eos_token_id=98,
pad_token_id=0,
num_mel_bins=80,
decoder_start_token_id=85,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.max_target_positions = max_target_positions
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.decoder_start_token_id = decoder_start_token_id
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length], self.vocab_size)
decoder_input_ids = torch.tensor(self.batch_size * [[self.decoder_start_token_id]], device=torch_device)
config = self.get_config()
inputs_dict = prepare_whisper_inputs_dict(
config,
attention_mask=None,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
)
return config, inputs_dict
def get_config(self):
return WhisperConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
max_target_positions=self.max_target_positions,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
decoder_start_token_id=self.decoder_start_token_id,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
def create_and_check_model_forward(self, config, inputs_dict, freeze_encoder=False):
model = WhisperModel(config=config).to(torch_device).eval()
if freeze_encoder:
model.freeze_encoder()
input_features = inputs_dict["input_features"]
decoder_input_ids = inputs_dict["decoder_input_ids"]
# first forward pass
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
self.parent.assertTrue(last_hidden_state.shape, (13, 7, 16))
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = WhisperModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["decoder_input_ids"]
attention_mask = inputs_dict["decoder_attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size).clamp(2)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = WhisperModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = WhisperEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_features"])[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = WhisperDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (WhisperModel, WhisperForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (WhisperForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"audio-classification": WhisperForAudioClassification,
"automatic-speech-recognition": WhisperForConditionalGeneration,
"feature-extraction": WhisperModel,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = False
test_pruning = False
test_missing_keys = False
# Needs higher percentages after model tester's vocab_size is changed to 200 (PR #21222)
# `0.5` is for `test_disk_offload` (which also works for `test_model_parallelism`)
model_split_percents = [0.5, 0.8, 0.9]
input_name = "input_features"
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name in [
"AutomaticSpeechRecognitionPipelineTests",
"AudioClassificationPipelineTests",
]:
# RuntimeError: The size of tensor a (1500) must match the size of tensor b (30) at non-singleton
# dimension 1
return True
return False
def setUp(self):
self.model_tester = WhisperModelTester(self)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_model_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs)
def test_model_forward_with_frozen_encoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_forward(*config_and_inputs, freeze_encoder=True)
def test_requires_grad_with_frozen_encoder(self):
config = self.model_tester.get_config()
for model_class in self.all_model_classes:
model = model_class(config)
model.freeze_encoder()
try:
encoder_grads = [param.requires_grad for param in model.encoder.parameters()]
decoder_grads = [param.requires_grad for param in model.decoder.parameters()]
except AttributeError:
encoder_grads = [param.requires_grad for param in model.model.encoder.parameters()]
decoder_grads = [param.requires_grad for param in model.model.decoder.parameters()]
self.assertFalse(all(encoder_grads))
self.assertTrue(all(decoder_grads))
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def _get_input_ids_and_config(self, batch_size=3):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict[self.input_name]
# cut to half length & take max batch_size=batch_size
input_ids = input_ids[:batch_size, :, :]
# generate max 3 tokens
max_length = 4
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
config.pad_token_id = config.eos_token_id
return config, input_ids, None, max_length
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
decoder_input_ids = inputs.pop("decoder_input_ids", None)
inputs.pop("decoder_attention_mask", None)
wte = model.get_input_embeddings()
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
# training is not supported yet
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
def test_generate_with_head_masking(self):
pass
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
config.max_target_positions = 400
input_features = input_dict["input_features"]
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
input_features = input_features.half()
model.half()
model.generate(input_features)
model.generate(input_features, num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def test_generate_language(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_features = input_dict["input_features"]
model = WhisperForConditionalGeneration(config).to(torch_device)
# Hack to keep the test fast and not require downloading a model with a generation_config
model.generation_config.__setattr__("lang_to_id", {"<|en|>": 1})
model.generation_config.__setattr__("task_to_id", {"transcribe": 2})
# test language code
model.generate(input_features, language="en")
# test tokenizer code
model.generate(input_features, language="<|en|>")
# test language name
model.generate(input_features, language="English")
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_features",
"attention_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
else:
seq_length = self.model_tester.seq_length
subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[subsampled_seq_length, self.model_tester.hidden_size],
)
if config.is_encoder_decoder:
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[decoder_seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", 1)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
decoder_key_length = getattr(self.model_tester, "decoder_key_length", 1)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length)
subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length)
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
out_len = len(outputs)
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
subsampled_encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 2
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length],
)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# make sure that decoder_input_ids are resized
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
if "decoder_input_ids" in inputs_dict:
inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_generate_without_input_ids(self):
pass
@staticmethod
def _get_encoder_outputs(
model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1
):
encoder = model.get_encoder()
encoder_outputs = encoder(
input_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.repeat_interleave(
num_interleave, dim=0
)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + torch.tensor(
[model._get_decoder_start_token_id()], device=input_ids.device
)
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1):
batch_size, mel, seq_length = input_ids.shape
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)
num_sequences_in_output = batch_size * num_return_sequences
gen_len = (
output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length
)
# scores
self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config)
# Attentions
# encoder
self._check_encoder_attention_for_generate(
output.encoder_attentions, batch_size, config, subsampled_seq_length
)
# decoder
self._check_attentions_for_generate(
num_sequences_in_output,
output.decoder_attentions,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
# Hidden States
# encoder
self._check_encoder_hidden_states_for_generate(
output.encoder_hidden_states, batch_size, config, subsampled_seq_length
)
# decoder
self._check_hidden_states_for_generate(
num_sequences_in_output,
output.decoder_hidden_states,
min_length=1,
max_length=output.sequences.shape[-1],
config=config,
use_cache=use_cache,
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
model.config.use_cache = False # FSTM still requires this hack -> FSTM should probably be refactored similar to BART afterward
input_features = inputs["input_features"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
# prepare `attention_mask` with shape (batch_size, sequence_length)
attention_mask = torch.ones(
input_features.shape[0],
input_features.shape[-1],
device=input_features.device,
dtype=input_features.dtype,
)
traced_model = torch.jit.trace(
model, (input_features, attention_mask, decoder_input_ids, decoder_attention_mask)
)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=5e-5, name="outputs", attributes=None):
# We override with a slightly higher tol value, as test recently became flaky
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, input_shape=init_shape, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
def test_mask_feature_prob(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.mask_feature_prob = 0.2
config.mask_feature_length = 2
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.train()
# forward pass
encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state
self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16))
def test_mask_time_prob(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.mask_time_prob = 0.2
config.mask_time_length = 2
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.train()
# forward pass
encoder_last_hidden_state = model(**input_dict).encoder_last_hidden_state
self.assertTrue(encoder_last_hidden_state.shape, (13, 30, 16))
def test_generate_with_prompt_ids_and_task_and_language(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
input_features = input_dict["input_features"]
prompt_ids = np.arange(5)
language = "<|de|>"
task = "translate"
lang_id = 6
task_id = 7
model.generation_config.__setattr__("lang_to_id", {language: lang_id})
model.generation_config.__setattr__("task_to_id", {task: task_id})
output = model.generate(input_features, max_new_tokens=5, task=task, language=language, prompt_ids=prompt_ids)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
lang_id,
task_id,
]
for row in output.tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = WhisperForConditionalGeneration(config).eval().to(torch_device)
input_features = input_dict["input_features"]
prompt_ids = np.asarray(range(5))
forced_decoder_ids = [(1, 6), (2, 7), (3, 8)]
output = model.generate(
input_features, max_new_tokens=5, forced_decoder_ids=forced_decoder_ids, prompt_ids=prompt_ids
)
expected_output_start = [
*prompt_ids.tolist(),
model.generation_config.decoder_start_token_id,
*[token for _rank, token in forced_decoder_ids],
]
for row in output.tolist():
self.assertListEqual(row[: len(expected_output_start)], expected_output_start)
@require_torch
@require_torchaudio
class WhisperModelIntegrationTests(unittest.TestCase):
@cached_property
def default_processor(self):
return WhisperProcessor.from_pretrained("openai/whisper-base")
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@slow
def test_tiny_logits_librispeech(self):
torch_device = "cpu"
set_seed(0)
model = WhisperModel.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
with torch.no_grad():
logits = model(
input_features,
decoder_input_ids=torch.tensor([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
use_cache=False,
)
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
2.9892, -6.7607, 5.7348, 3.6096, 0.2152, -5.7321, 4.8855, -1.6407,
0.2823, -1.5718, 10.4269, 3.4427, 0.0219, -8.0612, 3.4784, 8.4246,
4.0575, -2.2864, 11.1084, 0.9963, 0.9884, -8.5154, -3.5469, -9.3713,
0.9786, 3.5435, 7.4850, -5.2579, -1.4366, 10.4841
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
# fmt: off
EXPECTED_GENERATION = torch.tensor(
[
-1.4651, -2.6944, 2.7821, 2.3793, 4.0738, 0.0188, -3.3203, 1.9836,
0.0520, 0.7095, 1.1063, 0.2952, -3.6786, -0.5249, 0.3105, 4.7691,
1.1562, 1.3046, 0.5810, -0.3624, 1.7006, 1.3424, 0.9817, 2.1958,
1.8775, -5.7046, -0.7679, 4.0113, 2.6848, 2.8609
]
)
# fmt: on
head_logits = logits[0] @ model.decoder.embed_tokens.weight.T
self.assertTrue(torch.allclose(head_logits[0, 0, :30].cpu(), EXPECTED_GENERATION, atol=1e-4))
@slow
def test_small_en_logits_librispeech(self):
set_seed(0)
torch_device = "cpu"
model = WhisperModel.from_pretrained("openai/whisper-small.en")
model.to(torch_device)
input_speech = self._load_datasamples(1)
feaure_extractor = WhisperFeatureExtractor()
input_features = feaure_extractor(input_speech, return_tensors="pt").input_features.to(torch_device)
logits = model(
input_features,
decoder_input_ids=torch.tensor([[model.config.decoder_start_token_id]]),
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
-3.6784, -7.7211, -9.5070, -11.9286, -7.6489, -9.7026, -5.6188,
-8.0104, -4.6238, -5.1833, -9.0485, -3.4079, -5.4874, -2.6935,
-6.3479, -7.3398, -6.9558, -7.6867, -7.4748, -8.3463, -9.9781,
-10.8389, -10.3105, -11.7201, -9.7261, -7.1590, -5.9272, -12.4509,
-11.1146, -8.1918
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_large_logits_librispeech(self):
set_seed(0)
torch_device = "cpu"
model = WhisperModel.from_pretrained("openai/whisper-large")
model.to(torch_device)
input_speech = self._load_datasamples(1)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
processed_inputs = processor(
audio=input_speech, text="This part of the speech", add_special_tokens=False, return_tensors="pt"
)
input_features = processed_inputs.input_features.to(torch_device)
decoder_input_ids = processed_inputs.labels.to(torch_device)
logits = model(
input_features,
decoder_input_ids=decoder_input_ids,
output_hidden_states=False,
output_attentions=False,
use_cache=False,
)
logits = logits.last_hidden_state @ model.decoder.embed_tokens.weight.T
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
2.1382, 0.9381, 4.4671, 3.5589, 2.4022, 3.8576, -0.6521, 2.5472,
1.8301, 1.9957, 2.3432, 1.4678, 0.5459, 2.2597, 1.5179, 2.5357,
1.1624, 0.6194, 1.0757, 1.8259, 2.4076, 1.6601, 2.3503, 1.3376,
1.9891, 1.8635, 3.8931, 5.3699, 4.4772, 3.9184
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_tiny_en_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
model.config.decoder_start_token_id = 50257
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.batch_decode(generated_ids)[0]
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes, and we are glad to"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, num_beams=5, max_length=20)
transcript = processor.tokenizer.decode(generated_ids[0])
EXPECTED_TRANSCRIPT = (
"<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle"
" classes and we are glad"
)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_generation(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_generation_multilingual(self):
torch_device = "cpu"
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
model.to(torch_device)
ds = load_dataset("common_voice", "ja", split="test", streaming=True)
ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
input_speech = next(iter(ds))["audio"]["array"]
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = "木村さんに電話を貸してもらいました"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " Kimura-san called me."
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
generated_ids = model.generate(
input_features, do_sample=False, max_length=20, language="<|ja|>", task="translate"
)
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
EXPECTED_TRANSCRIPT = " I borrowed a phone from Kimura san"
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_large_batched_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-large")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features
generated_ids = model.generate(input_features, max_length=20, task="translate")
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
[50258, 50259, 50358, 50363, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 293, 321, 366, 5404],
[50258, 50259, 50358, 50363, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50257],
[50258, 50259, 50358, 50363, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904],
[50258, 50259, 50358, 50363, 634, 575, 12525, 22618, 1968, 6144, 35617, 20084, 1756, 311, 589, 307, 534, 10281, 934, 439]
]
)
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes and we are glad",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_en_batched_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, max_length=20).to("cpu")
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
[50257, 50362, 1770, 13, 2264, 346, 353, 318, 262, 46329, 286, 262, 3504, 6097, 11, 290, 356, 389, 9675, 284],
[50257, 50362, 5414, 318, 1770, 13, 2264, 346, 353, 338, 5642, 1342, 3499, 621, 465, 2300, 13, 50256, 50256, 50256],
[50257, 50362, 679, 4952, 514, 326, 379, 428, 43856, 1622, 286, 262, 614, 11, 351, 6786, 290, 32595, 12023, 28236],
[50257, 50362, 679, 468, 12296, 17188, 1771, 7361, 26113, 18881, 1122, 338, 670, 318, 1107, 8312, 706, 477, 290, 460]
]
)
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_LOGITS))
# fmt: off
EXPECTED_TRANSCRIPT = [
" Mr. Quilter is the apostle of the middle classes, and we are glad to",
" Nor is Mr. Quilter's manner less interesting than his matter.",
" He tells us that at this festive season of the year, with Christmas and roast beef looming",
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can",
]
# fmt: on
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertListEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_timestamp_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = np.concatenate(self._load_datasamples(4))
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generated_ids = model.generate(input_features, max_length=448, return_timestamps=True).to("cpu")
# fmt: off
EXPECTED_OUTPUT = torch.tensor([50258, 50259, 50359, 50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272, 293, 12904, 9256, 450, 10539, 51208, 51208, 949, 505, 11, 14138, 10117, 490, 3936, 293, 1080, 3542, 5160, 881, 26336, 281, 264, 1575, 13, 51552, 51552, 634, 575, 12525, 22618, 1968, 6144, 35617, 7354, 1292, 6, 589, 307, 534, 10281, 934, 439, 11, 293, 51836, 51836, 50257])
# fmt: on
self.assertTrue(torch.allclose(generated_ids, EXPECTED_OUTPUT))
EXPECTED_TRANSCRIPT = [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel. Nor is"
" Mr. Quilter's manner less interesting than his matter. He tells us that at this festive season"
" of the year, with Christmas and roast beef looming before us, similarly drawn from eating and"
" its results occur most readily to the mind. He has grave doubts whether Sir Frederick Latins'"
" work is really Greek after all, and"
),
"offsets": [
{
"text": (
" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
),
"timestamp": (0.0, 6.5600000000000005),
},
{
"text": " Nor is Mr. Quilter's manner less interesting than his matter.",
"timestamp": (6.5600000000000005, 11.24),
},
{
"text": (
" He tells us that at this festive season of the year, with Christmas and roast beef"
" looming"
),
"timestamp": (11.24, 16.88),
},
{
"text": (
" before us, similarly drawn from eating and its results occur most readily to the mind."
),
"timestamp": (16.88, 23.76),
},
{
"text": (
" He has grave doubts whether Sir Frederick Latins' work is really Greek after all, and"
),
"timestamp": (23.76, 29.44),
},
],
}
]
transcript = processor.batch_decode(generated_ids, skip_special_tokens=True, output_offsets=True)
self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
@slow
def test_tiny_token_timestamp_generation(self):
set_seed(0)
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
model.generation_config.alignment_heads = [[2, 2], [3, 0], [3, 2], [3, 3], [3, 4], [3, 5]]
input_speech = self._load_datasamples(4)
input_features = processor.feature_extractor(raw_speech=input_speech, return_tensors="pt").input_features.to(
torch_device
)
generate_outputs = model.generate(
input_features, max_length=448, return_timestamps=True, return_token_timestamps=True
)
self.assertEqual(generate_outputs.sequences.shape, generate_outputs.token_timestamps.shape)
# fmt: off
EXPECTED_OUTPUT = torch.tensor([
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.4800, 0.8200, 0.9600, 1.1200, 1.1200, 1.2200, 1.5000, 1.7200, 2.0000, 2.3400, 2.5000, 2.6600, 3.1800, 3.5600, 3.6800, 3.8000, 4.1000, 4.3000, 4.5800, 4.9400, 5.3800, 12.4200, 12.8400, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9200, 26.9400, 26.9400, 26.9400, 26.9400, 29.8400 ],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.5200, 0.9000, 1.1400, 1.4200, 1.5200, 1.6800, 1.6800, 1.8800, 2.1000, 2.2200, 2.6200, 3.1400, 3.5800, 3.9600, 4.4000, 17.3000, 17.3000, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7200, 26.7400, 26.7400, 26.7400, 26.7400, 26.7400, 26.7400, 28.0000 ],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7600, 1.0000, 1.4200, 1.8000, 1.9400, 2.1800, 2.5200, 3.0200, 3.3200, 3.5400, 3.9400, 4.5600, 4.9200, 5.2800, 5.5600, 5.9000, 6.1600, 6.3000, 6.4800, 6.4800, 6.6400, 7.8200, 7.9600, 8.2200, 8.6000, 8.9200, 9.2200, 9.5200, 9.7200, 10.0600, 10.5400, 10.8800, 11.2600, 11.5400, 11.7400, 12.0800, 15.6800, 15.6800],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.7400, 1.0400, 1.3200, 1.6800, 2.1400, 2.4800, 2.7800, 3.0800, 3.1600, 3.4000, 3.6000, 4.0200, 4.2200, 4.8600, 5.2400, 5.7400, 6.3400, 6.6200, 6.7600, 6.7600, 6.8600, 7.2400, 7.4200, 7.6800, 7.9200, 8.4800, 8.7600, 9.2000, 9.2000, 9.4200, 15.8200, 15.8200, 29.6400, 29.6600, 29.6600, 29.6600, 29.6600, 29.7600]
])
# fmt: on
self.assertTrue(torch.allclose(generate_outputs.token_timestamps.to("cpu"), EXPECTED_OUTPUT))
@slow
def test_tiny_specaugment_librispeech(self):
torch_device = "cpu"
set_seed(0)
# Apply SpecAugment
model = WhisperModel.from_pretrained("openai/whisper-tiny", apply_spec_augment=True)
# Set model to training mode to enable SpecAugment
model.train()
model.to(torch_device)
input_speech = self._load_datasamples(1)
feature_extractor = WhisperFeatureExtractor()
input_features = feature_extractor(input_speech, return_tensors="pt").input_features
with torch.no_grad():
logits = model(
input_features,
decoder_input_ids=torch.tensor([[50258, 50259, 50359]]),
output_hidden_states=False,
output_attentions=False,
return_dict=False,
use_cache=False,
)
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
0.9362, -4.7105, 5.0879, 3.9642, 1.0013, -6.0096, 4.7285, -3.1847,
-0.8648, 1.9631, 6.2653, 3.6936, 0.3575, -4.5818, 3.0564, 7.8712,
2.9951, 0.6848, 9.9497, -2.6638, 1.1571, -6.8546, -1.4333, -7.7584,
1.1200, 3.9030, 4.4655, -4.4919, -1.1703, 9.6241
]
)
# fmt: on
self.assertTrue(torch.allclose(logits[0][0, 0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_generate_with_prompt_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(4)[-1:]
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
output_without_prompt = model.generate(input_features)
prompt_ids = processor.get_prompt_ids("Leighton")
output_with_prompt = model.generate(input_features, prompt_ids=prompt_ids)
expected_without_prompt = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|> He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca.<|endoftext|>"
expected_with_prompt = "<|startofprev|> Leighton<|startoftranscript|><|en|><|transcribe|><|notimestamps|> He has grave doubts whether Sir Frederick Leighton's work is really Greek after all and can discover in it but little of Rocky Ithaca.<|endoftext|>"
self.assertEqual(processor.decode(output_without_prompt[0]), expected_without_prompt)
self.assertEqual(processor.decode(output_with_prompt[0]), expected_with_prompt)
@slow
def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
task = "translate"
language = "de"
expected_tokens = [f"<|{task}|>", f"<|{language}|>"]
prompt = "test prompt"
prompt_ids = processor.get_prompt_ids(prompt)
output = model.generate(input_features, task=task, language=language, prompt_ids=prompt_ids)
text = processor.decode(output[0])
self.assertTrue(prompt in text)
self.assertTrue(all(token in text for token in expected_tokens))
@slow
def test_generate_with_prompt_ids_and_no_non_prompt_forced_decoder_ids(self):
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
model.to(torch_device)
input_speech = self._load_datasamples(1)
input_features = processor(input_speech, return_tensors="pt").input_features.to(torch_device)
prompt = "test prompt"
prompt_ids = processor.get_prompt_ids(prompt)
model.generation_config.forced_decoder_ids = None
model.config.forced_decoder_ids = None
output = model.generate(input_features, prompt_ids=prompt_ids, return_timestamps=True)
text = processor.decode(output[0])
self.assertTrue(prompt in text)
def prepare_whisper_encoder_inputs_dict(config, input_features, head_mask=None):
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
return {"input_features": input_features, "head_mask": head_mask}
@require_torch
class WhisperEncoderModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=60,
is_training=True,
use_labels=True,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
input_channels=1,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
max_source_positions=30,
num_mel_bins=80,
num_conv_layers=1,
suppress_tokens=None,
begin_suppress_tokens=None,
classifier_proj_size=4,
num_labels=2,
is_encoder_decoder=False,
is_decoder=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.input_channels = input_channels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_mel_bins = num_mel_bins
self.max_position_embeddings = max_position_embeddings
self.max_source_positions = max_source_positions
self.num_conv_layers = num_conv_layers
self.suppress_tokens = suppress_tokens
self.begin_suppress_tokens = begin_suppress_tokens
self.classifier_proj_size = classifier_proj_size
self.num_labels = num_labels
self.is_encoder_decoder = is_encoder_decoder
self.is_decoder = is_decoder
def get_config(self):
return WhisperConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
input_channels=self.input_channels,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
max_source_positions=self.max_source_positions,
decoder_ffn_dim=self.hidden_size,
encoder_ffn_dim=self.hidden_size,
suppress_tokens=self.suppress_tokens,
begin_suppress_tokens=self.begin_suppress_tokens,
classifier_proj_size=self.classifier_proj_size,
num_labels=self.num_labels,
is_encoder_decoder=self.is_encoder_decoder,
is_decoder=self.is_decoder,
)
def prepare_config_and_inputs(self):
input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.seq_length])
config = self.get_config()
inputs_dict = prepare_whisper_encoder_inputs_dict(
config,
input_features=input_features,
)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def get_subsampled_output_lengths(self, input_lengths):
"""
Computes the output length of the convolutional layers
"""
for i in range(self.num_conv_layers):
input_lengths = (input_lengths - 1) // 2 + 1
return input_lengths
@property
def encoder_seq_length(self):
return self.get_subsampled_output_lengths(self.seq_length)
def create_and_check_model_forward(self, config, inputs_dict, freeze_encoder=False):
model = WhisperForAudioClassification(config=config).to(torch_device).eval()
if freeze_encoder:
model.freeze_encoder()
input_features = inputs_dict["input_features"]
# first forward pass
last_hidden_state = model(input_features).logits
self.parent.assertTrue(last_hidden_state.shape, (13, 2))
@require_torch
class WhisperEncoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (WhisperForAudioClassification,) if is_torch_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_pruning = False
test_missing_keys = False
input_name = "input_features"
def setUp(self):
self.model_tester = WhisperEncoderModelTester(self)
self.config_tester = ConfigTester(self, config_class=WhisperConfig)
self.maxDiff = 3000
def test_config(self):
self.config_tester.run_common_tests()
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_features", "head_mask", "encoder_outputs"]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_cpu_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_disk_offload(self):
pass
@unittest.skip(reason="Some undefined behavior encountered with tiny versions of this model. Skip for now.")
def test_model_parallelism(self):
pass
# input embeds is meaningless for an encoder-only acoustic model
def test_inputs_embeds(self):
pass
# the equivalent test is passing the encoder outputs directly to the model
def test_encoder_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
with torch.no_grad():
outputs = model(**inputs)[0]
input_ids = inputs["input_features"]
del inputs["input_features"]
encoder = model.encoder
with torch.no_grad():
inputs["encoder_outputs"] = encoder(input_ids)
outputs_embeds = model(**inputs)[0]
self.assertTrue((outputs_embeds == outputs).all())
# Needs to override as the encoder input embedding is a Conv1d
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (torch.nn.Conv1d))
model.set_input_embeddings(torch.nn.Conv1d(10, 10, 3))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, torch.nn.Conv1d))
# WhisperEncoder cannot resize token embeddings since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, input_shape=init_shape, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs_loaded.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs_loaded, pt_outputs, model_class)
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
init_shape = (1,) + inputs_dict["input_features"].shape[1:]
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
fx_model_class_name = "Flax" + model_class.__name__
if not hasattr(transformers, fx_model_class_name):
# no flax model exists for this class
return
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
fx_model_class = getattr(transformers, fx_model_class_name)
# load PyTorch class
pt_model = model_class(config).eval()
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
pt_model.config.use_cache = False
# load Flax class
fx_model = fx_model_class(config, input_shape=init_shape, dtype=jnp.float32)
# make sure only flax inputs are forward that actually exist in function args
fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys()
# prepare inputs
pt_inputs = self._prepare_for_class(inputs_dict, model_class)
# remove function args that don't exist in Flax
pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys}
# send pytorch inputs to the correct device
pt_inputs = {
k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs.items()
}
# convert inputs to Flax
fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)}
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
# make sure weights are tied in PyTorch
pt_model.tie_weights()
# send pytorch model to the correct device
pt_model.to(torch_device)
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
fx_outputs = fx_model(**fx_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs, model_class)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True)
# send pytorch model to the correct device
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
fx_keys = tuple([k for k, v in fx_outputs.items() if v is not None])
pt_keys = tuple([k for k, v in pt_outputs_loaded.items() if v is not None])
self.assertEqual(fx_keys, pt_keys)
self.check_pt_flax_outputs(fx_outputs, pt_outputs_loaded, model_class)
| 84,724 | 42.74032 | 640 | py |
transformers | transformers-main/tests/models/whisper/test_processor_whisper.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import pytest
from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
from .test_feature_extraction_whisper import floats_list
if is_speech_available():
from transformers import WhisperFeatureExtractor, WhisperProcessor
TRANSCRIBE = 50358
NOTIMESTAMPS = 50362
@require_torch
@require_torchaudio
@require_sentencepiece
class WhisperProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "openai/whisper-small.en"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = WhisperProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = WhisperProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, WhisperTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
def test_get_decoder_prompt_ids(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", no_timestamps=True)
self.assertIsInstance(forced_decoder_ids, list)
for ids in forced_decoder_ids:
self.assertIsInstance(ids, (list, tuple))
expected_ids = [TRANSCRIBE, NOTIMESTAMPS]
self.assertListEqual([ids[-1] for ids in forced_decoder_ids], expected_ids)
def test_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("Mr. Quilter")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 1770, 13, 2264, 346, 353])
self.assertEqual(decoded_prompt, "<|startofprev|> Mr. Quilter")
def test_empty_get_prompt_ids(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
prompt_ids = processor.get_prompt_ids("")
decoded_prompt = processor.tokenizer.decode(prompt_ids)
self.assertListEqual(prompt_ids.tolist(), [50360, 220])
self.assertEqual(decoded_prompt, "<|startofprev|> ")
def test_get_prompt_ids_with_special_tokens(self):
processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
def _test_prompt_error_raised_helper(prompt, special_token):
with pytest.raises(ValueError) as excinfo:
processor.get_prompt_ids(prompt)
expected = f"Encountered text in the prompt corresponding to disallowed special token: {special_token}."
self.assertEqual(expected, str(excinfo.value))
_test_prompt_error_raised_helper("<|startofprev|> test", "<|startofprev|>")
_test_prompt_error_raised_helper("test <|notimestamps|>", "<|notimestamps|>")
_test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>", "<|zh|>")
| 7,476 | 40.538889 | 117 | py |
transformers | transformers-main/tests/models/bloom/test_modeling_bloom.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import math
import unittest
from transformers import BloomConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomTokenizerFast,
)
@require_torch
class BloomModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=False,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_dropout_prob = attention_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
def get_large_model_config(self):
return BloomConfig.from_pretrained("bigscience/bloom")
def prepare_config_and_inputs(self, gradient_checkpointing=False):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config(gradient_checkpointing=gradient_checkpointing)
return (config, input_ids, input_mask, sequence_labels)
def get_config(self, gradient_checkpointing=False, slow_but_exact=True):
return BloomConfig(
vocab_size=self.vocab_size,
seq_length=self.seq_length,
hidden_size=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
hidden_dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
num_labels=self.num_labels,
gradient_checkpointing=gradient_checkpointing,
slow_but_exact=slow_but_exact,
dtype="float32",
)
def create_and_check_bloom_model(self, config, input_ids, input_mask, *args):
model = BloomModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_bloom_model_past(self, config, input_ids, input_mask, *args):
model = BloomModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, attention_mask=torch.ones_like(input_ids), use_cache=True)
outputs_use_cache_conf = model(input_ids, attention_mask=torch.ones_like(input_ids))
outputs_no_past = model(input_ids, use_cache=False, attention_mask=torch.ones_like(input_ids))
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_bloom_model_attention_mask_past(self, config, input_ids, input_mask, *args):
model = BloomModel(config=config)
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = self.seq_length // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_bloom_model_past_large_inputs(self, config, input_ids, input_mask, *args):
model = BloomModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, attention_mask=input_mask, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past)[
"last_hidden_state"
]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, *args):
model = BloomForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_sequence_classification_model(self, config, input_ids, input_mask, *args):
config.num_labels = self.num_labels
model = BloomForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_token_classification_model(self, config, input_ids, input_mask, *args):
model = BloomForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_question_answering_model(self, config, input_ids, input_mask, *args):
model = BloomForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_forward_and_backwards(
self, config, input_ids, input_mask, *args, gradient_checkpointing=False
):
model = BloomForCausalLM(config)
model.to(torch_device)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
result = model(input_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def create_and_check_bloom_weight_initialization(self, config, *args):
model = BloomModel(config)
model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer)
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001)
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask, sequence_labels = config_and_inputs
inputs_dict = {"input_ids": input_ids}
return config, inputs_dict
@require_torch
class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
BloomModel,
BloomForCausalLM,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (BloomForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": BloomModel,
"question-answering": BloomForQuestionAnswering,
"text-classification": BloomForSequenceClassification,
"text-generation": BloomForCausalLM,
"token-classification": BloomForTokenClassification,
"zero-shot": BloomForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
test_missing_keys = False
test_pruning = False
test_torchscript = True # torch.autograd functions seems to be not supported
def setUp(self):
self.model_tester = BloomModelTester(self)
self.config_tester = ConfigTester(self, config_class=BloomConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_bloom_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bloom_model(*config_and_inputs)
def test_bloom_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bloom_model_past(*config_and_inputs)
def test_bloom_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bloom_model_attention_mask_past(*config_and_inputs)
def test_bloom_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bloom_model_past_large_inputs(*config_and_inputs)
def test_bloom_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_bloom_sequence_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs)
def test_bloom_token_classification_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_token_classification_model(*config_and_inputs)
def test_bloom_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
def test_bloom_weight_initialization(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bloom_weight_initialization(*config_and_inputs)
@unittest.skip("Bloom has a non-standard KV cache format.")
def test_past_key_values_format(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BloomModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
@require_torch_gpu
def test_simple_generation(self):
# This test is a bit flaky. For some GPU architectures, pytorch sets by default allow_fp16_reduced_precision_reduction = True and some operations
# do not give the same results under this configuration, especially torch.baddmm and torch.bmm. https://pytorch.org/docs/stable/notes/numerical_accuracy.html#fp16-on-mi200
# As we leave the default value (True) for allow_fp16_reduced_precision_reduction , the tests failed when running in half-precision with smaller models (560m)
# Please see: https://pytorch.org/docs/stable/notes/cuda.html#reduced-precision-reduction-in-fp16-gemms
# This discrepancy is observed only when using small models and seems to be stable for larger models.
# Our conclusion is that these operations are flaky for small inputs but seems to be stable for larger inputs (for the functions `baddmm` and `bmm`), and therefore for larger models.
# Here is a summary of an ablation study of our observations
# EXPECTED_OUTPUT = "I enjoy walking with my cute dog, and I love to watch the kids play. I am a very active person, and I am a very good listener. I am a very good person, and I am a very good person. I am a"
# 560m + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS
# 560m + allow_fp16_reduced_precision_reduction = False + torch.baddm ==> PASS
# 560m + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS
# 560m + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> FAIL
# EXPECTED_OUTPUT = "I enjoy walking with my cute dog, but I also enjoy hiking, biking, and swimming. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love to cook and bake. I love"
# >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.baddm ==> PASS (for use_cache=True and use_cache=False)
# >=1b1 + allow_fp16_reduced_precision_reduction = True + torch.bmm ==> PASS
# >=1b1 + allow_fp16_reduced_precision_reduction = False + torch.bmm ==> PASS
path_560m = "bigscience/bloom-560m"
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").cuda()
model = model.eval()
tokenizer = BloomTokenizerFast.from_pretrained(path_560m)
input_sentence = "I enjoy walking with my cute dog"
# This output has been obtained using fp32 model on the huggingface DGX workstation - NVIDIA A100 GPU
EXPECTED_OUTPUT = (
"I enjoy walking with my cute dog, and I love to watch the kids play with the kids. I am a very "
"active person, and I enjoy working out, and I am a very active person. I am a very active person, and I"
)
input_ids = tokenizer.encode(input_sentence, return_tensors="pt")
greedy_output = model.generate(input_ids.cuda(), max_length=50)
self.assertEqual(tokenizer.decode(greedy_output[0], skip_special_tokens=True), EXPECTED_OUTPUT)
@slow
@require_torch_gpu
def test_batch_generation(self):
path_560m = "bigscience/bloom-560m"
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").cuda()
model = model.eval()
tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
input_sentence = ["I enjoy walking with my cute dog", "I enjoy walking with my cute dog"]
input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
greedy_output = model.generate(
input_ids["input_ids"].cuda(), attention_mask=input_ids["attention_mask"], max_length=50, do_sample=False
)
self.assertEqual(
tokenizer.decode(greedy_output[0], skip_special_tokens=True),
tokenizer.decode(greedy_output[1], skip_special_tokens=True),
)
@slow
@require_torch_gpu
def test_batch_generation_padd(self):
path_560m = "bigscience/bloom-560m"
model = BloomForCausalLM.from_pretrained(path_560m, use_cache=True, revision="gs555750").cuda()
model = model.eval()
tokenizer = BloomTokenizerFast.from_pretrained(path_560m, padding_side="left")
input_sentence = ["I enjoy walking with my cute dog", "Hello my name is"]
input_sentence_without_pad = "Hello my name is"
input_ids = tokenizer.batch_encode_plus(input_sentence, return_tensors="pt", padding=True)
input_ids_without_pad = tokenizer.encode(input_sentence_without_pad, return_tensors="pt")
greedy_output = model.generate(
input_ids["input_ids"].cuda(), attention_mask=input_ids["attention_mask"], max_length=50, do_sample=False
)
greedy_output_without_pad = model.generate(input_ids_without_pad.cuda(), max_length=50, do_sample=False)
# test token values
self.assertEqual(greedy_output[-1, 3:].tolist(), greedy_output_without_pad[0, :-3].tolist())
# test reconstructions
self.assertEqual(
tokenizer.decode(greedy_output[-1, 3:], skip_special_tokens=True),
tokenizer.decode(greedy_output_without_pad[0, :-3], skip_special_tokens=True),
)
@require_torch
class BloomEmbeddingTest(unittest.TestCase):
"""
The goal here is to compare the embeddings generated by the model trained
using Megatron-LM with the one from the transformers library, with a small GPT2-like model
to ensure that the conversion from Megatron-LM to transformers has been done successfully.
The script compares the logits of the embedding layer and the transformer layers.
WARNING: It is expected that these logits will not have exactly the same statistics when running
the code on CPU or GPU. For more info, please visit:
- https://github.com/pytorch/pytorch/issues/76052#issuecomment-1103193548
- https://discuss.pytorch.org/t/reproducibility-issue-between-intel-and-amd-cpus/144779/9
You need to install tokenizers following this readme:
- https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
Tokenizer used during training:
- https://huggingface.co/bigscience-catalogue-data-dev/byte-level-bpe-tokenizer-no-norm-250k-whitespace-and-eos-regex-alpha-v3-dedup-lines-articles
# TODO change the script (or just add skip) when building the env with tokenizers 0.12.0
"""
def setUp(self):
super().setUp()
self.path_bigscience_model = "bigscience/bigscience-small-testing"
@require_torch
def test_embeddings(self):
# The config in this checkpoint has `bfloat16` as `torch_dtype` -> model in `bfloat16`
model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, torch_dtype="auto")
model.eval()
EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN = {
3478: 0.0002307891845703125,
368: -0.000568389892578125,
109586: -0.0003910064697265625,
35433: -0.000194549560546875,
2: 0.0004138946533203125,
77: 0.000659942626953125,
132619: -0.00031280517578125,
2175: 0.000457763671875,
23714: 0.000263214111328125,
73173: -0.000286102294921875,
144252: 0.00052642822265625,
}
EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN = {
3478: -0.00921630859375,
368: -0.010009765625,
109586: -0.01031494140625,
35433: -0.01177978515625,
2: -0.0074462890625,
77: -0.00848388671875,
132619: -0.009521484375,
2175: -0.0074462890625,
23714: -0.0145263671875,
73173: -0.007415771484375,
144252: -0.01007080078125,
}
EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX = {
3478: 0.0128173828125,
368: 0.01214599609375,
109586: 0.0111083984375,
35433: 0.01019287109375,
2: 0.0157470703125,
77: 0.0174560546875,
132619: 0.0078125,
2175: 0.0113525390625,
23714: 0.0146484375,
73173: 0.01116943359375,
144252: 0.01141357421875,
}
EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM = {"value": 0.08203125}
EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN = {
132619: -0.00031256675720214844,
3478: 0.00023090839385986328,
368: -0.0005702972412109375,
109586: -0.00039124488830566406,
35433: -0.000194549560546875,
2: 0.0004146099090576172,
2175: 0.0004572868347167969,
23714: 0.00026416778564453125,
73173: -0.0002865791320800781,
144252: 0.0005254745483398438,
77: 0.0006618499755859375,
}
EMBEDDINGS_DS_BEFORE_LN_F_16_MIN = {
3478: -0.00921630859375,
368: -0.010009765625,
109586: -0.01031494140625,
35433: -0.01177978515625,
2: -0.0074462890625,
77: -0.00848388671875,
132619: -0.009521484375,
2175: -0.0074462890625,
23714: -0.0145263671875,
73173: -0.007415771484375,
144252: -0.01007080078125,
}
EMBEDDINGS_DS_BEFORE_LN_F_16_MAX = {
3478: 0.0128173828125,
368: 0.01214599609375,
109586: 0.0111083984375,
35433: 0.01019287109375,
2: 0.0157470703125,
77: 0.0174560546875,
132619: 0.0078125,
2175: 0.0113525390625,
23714: 0.0146484375,
73173: 0.01116943359375,
144252: 0.01141357421875,
}
EMBEDDINGS_DS_BEFORE_LN_F_16_SUM = {"value": 0.0821533203125}
EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN = {
132619: -0.00031267106533050537,
3478: 0.00023087859153747559,
368: -0.0005701072514057159,
109586: -0.0003911703824996948,
35433: -0.0001944899559020996,
2: 0.0004146844148635864,
2175: 0.00045740045607089996,
23714: 0.0002641640603542328,
73173: -0.0002864748239517212,
144252: 0.0005256589502096176,
77: 0.0006617321632802486,
}
EMBEDDINGS_DS_BEFORE_LN_F_32_MIN = {
3478: -0.00921630859375,
368: -0.010009765625,
109586: -0.01031494140625,
35433: -0.01177978515625,
2: -0.0074462890625,
77: -0.00848388671875,
132619: -0.009521484375,
2175: -0.0074462890625,
23714: -0.0145263671875,
73173: -0.007415771484375,
144252: -0.01007080078125,
}
EMBEDDINGS_DS_BEFORE_LN_F_32_MAX = {
3478: 0.0128173828125,
368: 0.01214599609375,
109586: 0.0111083984375,
35433: 0.01019287109375,
2: 0.0157470703125,
77: 0.0174560546875,
132619: 0.0078125,
2175: 0.0113525390625,
23714: 0.0146484375,
73173: 0.01116943359375,
144252: 0.01141357421875,
}
EMBEDDINGS_DS_BEFORE_LN_F_32_SUM = {"value": 0.08217757940292358}
TEST_EMBEDDINGS = {
"torch.bfloat16": {
"mean": EMBEDDINGS_DS_BEFORE_LN_BF_16_MEAN,
"max": EMBEDDINGS_DS_BEFORE_LN_BF_16_MAX,
"min": EMBEDDINGS_DS_BEFORE_LN_BF_16_MIN,
"sum": EMBEDDINGS_DS_BEFORE_LN_BF_16_SUM,
},
"torch.float32": {
"mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
"max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
"min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
"sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
},
"torch.float": {
"mean": EMBEDDINGS_DS_BEFORE_LN_F_32_MEAN,
"max": EMBEDDINGS_DS_BEFORE_LN_F_32_MAX,
"min": EMBEDDINGS_DS_BEFORE_LN_F_32_MIN,
"sum": EMBEDDINGS_DS_BEFORE_LN_F_32_SUM,
},
"torch.float16": {
"mean": EMBEDDINGS_DS_BEFORE_LN_F_16_MEAN,
"max": EMBEDDINGS_DS_BEFORE_LN_F_16_MAX,
"min": EMBEDDINGS_DS_BEFORE_LN_F_16_MIN,
"sum": EMBEDDINGS_DS_BEFORE_LN_F_16_SUM,
},
}
# fmt: off
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
# fmt: on
EMBEDDINGS_DS_AFTER_LN_MEAN = {
3478: -6.580352783203125e-05,
368: 0.0001316070556640625,
109586: -0.00030517578125,
35433: 4.00543212890625e-05,
2: -7.2479248046875e-05,
77: -8.96453857421875e-05,
132619: 0.0001583099365234375,
2175: 2.1219253540039062e-05,
23714: -0.000247955322265625,
73173: -0.00021839141845703125,
144252: -0.0001430511474609375,
}
EMBEDDINGS_DS_AFTER_LN_MIN = {
3478: -1.6953125,
368: -1.6875,
109586: -1.6875,
35433: -2.125,
2: -1.390625,
77: -1.5390625,
132619: -1.875,
2175: -1.4609375,
23714: -2.296875,
73173: -1.3515625,
144252: -1.78125,
}
EMBEDDINGS_DS_AFTER_LN_MAX = {
3478: 2.265625,
368: 2.28125,
109586: 1.953125,
35433: 1.90625,
2: 2.703125,
77: 2.828125,
132619: 1.65625,
2175: 2.015625,
23714: 2.234375,
73173: 2.171875,
144252: 1.828125,
}
EMBEDDINGS_DS_AFTER_LN = {
"mean": EMBEDDINGS_DS_AFTER_LN_MEAN,
"min": EMBEDDINGS_DS_AFTER_LN_MIN,
"max": EMBEDDINGS_DS_AFTER_LN_MAX,
}
tensor_ids = torch.LongTensor([EXAMPLE_IDS])
with torch.no_grad():
embeddings = model.transformer.word_embeddings(tensor_ids)
embeddings_ln = model.transformer.word_embeddings_layernorm(embeddings) #
# first check the embeddings before LN
output_dict = {"min": {}, "max": {}, "mean": {}, "sum": {"value": embeddings.sum().item()}}
for i, idx in enumerate(EXAMPLE_IDS):
output_dict["min"][idx] = embeddings.min(dim=-1).values[0][i].item()
output_dict["max"][idx] = embeddings.max(dim=-1).values[0][i].item()
output_dict["mean"][idx] = embeddings.mean(dim=-1)[0][i].item()
for key in TEST_EMBEDDINGS[str(model.dtype)].keys():
self.assertDictEqual(TEST_EMBEDDINGS[str(model.dtype)][key], output_dict[key])
output_dict_norm = {"min": {}, "max": {}, "mean": {}}
for i, idx in enumerate(EXAMPLE_IDS):
output_dict_norm["min"][idx] = embeddings_ln.min(dim=-1).values[0][i].item()
output_dict_norm["max"][idx] = embeddings_ln.max(dim=-1).values[0][i].item()
output_dict_norm["mean"][idx] = embeddings_ln.mean(dim=-1)[0][i].item()
# This test does not pass when places = 2
for i, key in enumerate(output_dict_norm.keys()):
for j, idx in enumerate(output_dict[key].keys()):
self.assertAlmostEqual(EMBEDDINGS_DS_AFTER_LN[key][idx], output_dict_norm[key][idx], places=1)
@require_torch
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(
torch_device
)
model.eval()
# fmt: off
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
# fmt: on
MEAN_VALUE_LAST_LM = -4.3392181396484375e-05
MIN_MAX_DICT = {"min": -2.0625, "max": 2.75}
tensor_ids = torch.LongTensor([EXAMPLE_IDS])
with torch.no_grad():
logits = model(tensor_ids.to(torch_device))
output_dict = {
"min": logits.last_hidden_state.min(dim=-1).values[0][0].item(),
"max": logits.last_hidden_state.max(dim=-1).values[0][0].item(),
}
if cuda_available:
self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=4)
else:
self.assertAlmostEqual(MEAN_VALUE_LAST_LM, logits.last_hidden_state.mean().item(), places=3)
self.assertDictEqual(MIN_MAX_DICT, output_dict)
@require_torch
def test_logits(self):
cuda_available = torch.cuda.is_available()
model = BloomForCausalLM.from_pretrained(self.path_bigscience_model, use_cache=False, torch_dtype="auto").to(
torch_device
) # load in bf16
model.eval()
# fmt: off
EXAMPLE_IDS = [3478, 368, 109586, 35433, 2, 77, 132619, 3478, 368, 109586, 35433, 2, 2175, 23714, 73173, 144252, 2, 77, 132619, 3478]
# fmt: on
MEAN_LOGITS_GPU_1 = -1.823902130126953e-05
MEAN_LOGITS_GPU_2 = 1.9431114196777344e-05
tensor_ids = torch.LongTensor([EXAMPLE_IDS]).to(torch_device)
with torch.no_grad():
output = model(tensor_ids).logits
output_gpu_1, output_gpu_2 = output.split(125440, dim=-1)
if cuda_available:
self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6)
self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)
else:
self.assertAlmostEqual(output_gpu_1.mean().item(), MEAN_LOGITS_GPU_1, places=6) # 1e-06 precision!!
self.assertAlmostEqual(output_gpu_2.mean().item(), MEAN_LOGITS_GPU_2, places=6)
| 34,784 | 42.21118 | 217 | py |
transformers | transformers-main/tests/models/reformer/test_tokenization_reformer.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class ReformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = ReformerTokenizer
rust_tokenizer_class = ReformerTokenizerFast
test_rust_tokenizer = True
test_seq2seq = False
test_sentencepiece = True
def setUp(self):
super().setUp()
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<s>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<unk>")
self.assertEqual(vocab_keys[1], "<s>")
self.assertEqual(vocab_keys[-1], "j")
self.assertEqual(len(vocab_keys), 1_000)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_000)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_padding(self, max_length=15):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Simple input
s = "This is a simple input"
s2 = ["This is a simple input 1", "This is a simple input 2"]
p = ("This is a simple input", "This is a pair")
p2 = [
("This is a simple input 1", "This is a simple input 2"),
("This is a simple pair 1", "This is a simple pair 2"),
]
# Simple input tests
self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length")
# Simple input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
s2,
max_length=max_length,
padding="max_length",
)
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length")
# Pair input
self.assertRaises(
ValueError,
tokenizer_r.batch_encode_plus,
p2,
max_length=max_length,
padding="max_length",
)
# tokenizer has no padding token
def test_padding_different_model_input_name(self):
pass
def test_full_tokenizer(self):
tokenizer = ReformerTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [126, 32, 262, 152, 38, 72, 287]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
original_tokenizer_encodings = [
108,
265,
24,
111,
4,
258,
156,
35,
28,
275,
3,
259,
297,
260,
84,
4,
35,
110,
44,
8,
259,
91,
268,
21,
11,
209,
274,
109,
266,
277,
117,
86,
93,
315,
258,
278,
258,
277,
258,
0,
258,
288,
258,
319,
258,
0,
258,
0,
258,
0,
258,
0,
258,
287,
258,
315,
258,
289,
258,
278,
99,
269,
266,
262,
8,
259,
241,
4,
217,
230,
268,
266,
55,
168,
106,
75,
193,
266,
223,
27,
49,
26,
282,
25,
264,
299,
19,
26,
0,
258,
277,
117,
86,
93,
176,
183,
270,
11,
262,
42,
61,
265,
]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt")
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus([sequence, sequence], return_tensors="pt")
config = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
config.axial_pos_shape = encoded_sequence["input_ids"].shape
model = ReformerModel(config)
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
sequences = [
"This is a very simple sentence.",
"The quick brown fox jumps over the lazy dog.",
]
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/reformer-crime-and-punishment",
revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a",
padding=False,
sequences=sequences,
)
| 11,946 | 31.029491 | 402 | py |
transformers | transformers-main/tests/models/reformer/test_modeling_reformer.py | # coding=utf-8 # Copyright 2020 Huggingface
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import ReformerConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
require_torch_multi_gpu,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ReformerForMaskedLM,
ReformerForQuestionAnswering,
ReformerForSequenceClassification,
ReformerLayer,
ReformerModel,
ReformerModelWithLMHead,
ReformerTokenizer,
)
class ReformerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=32,
is_training=True,
is_decoder=True,
use_input_mask=True,
use_labels=True,
vocab_size=32,
attention_head_size=16,
hidden_size=32,
num_attention_heads=2,
local_attn_chunk_length=4,
local_num_chunks_before=1,
local_num_chunks_after=0,
num_buckets=None,
num_hashes=1,
lsh_attn_chunk_length=None,
lsh_num_chunks_before=None,
lsh_num_chunks_after=None,
chunk_size_lm_head=0,
chunk_size_feed_forward=0,
feed_forward_size=32,
hidden_act="gelu",
hidden_dropout_prob=0.1,
local_attention_probs_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=None,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 16],
attn_layers=["local", "local", "local", "local"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=0,
num_labels=2,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.is_decoder = is_decoder
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.attention_head_size = attention_head_size
self.hidden_size = hidden_size
self.num_attention_heads = num_attention_heads
self.num_hidden_layers = len(attn_layers) if attn_layers is not None else 0
self.local_attn_chunk_length = local_attn_chunk_length
self.local_num_chunks_after = local_num_chunks_after
self.local_num_chunks_before = local_num_chunks_before
self.num_hashes = num_hashes
self.num_buckets = tuple(num_buckets) if isinstance(num_buckets, list) else num_buckets
self.lsh_attn_chunk_length = lsh_attn_chunk_length
self.lsh_num_chunks_after = lsh_num_chunks_after
self.lsh_num_chunks_before = lsh_num_chunks_before
self.hidden_act = hidden_act
self.feed_forward_size = feed_forward_size
self.hidden_dropout_prob = hidden_dropout_prob
self.local_attention_probs_dropout_prob = local_attention_probs_dropout_prob
self.lsh_attention_probs_dropout_prob = lsh_attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.axial_pos_embds = axial_pos_embds
self.axial_pos_shape = tuple(axial_pos_shape)
self.axial_pos_embds_dim = tuple(axial_pos_embds_dim)
self.axial_norm_std = axial_norm_std
self.chunk_size_lm_head = chunk_size_lm_head
self.chunk_size_feed_forward = chunk_size_feed_forward
self.scope = scope
self.attn_layers = attn_layers
self.pad_token_id = pad_token_id
self.hash_seed = hash_seed
attn_chunk_length = local_attn_chunk_length if local_attn_chunk_length is not None else lsh_attn_chunk_length
num_chunks_after = local_num_chunks_after if local_num_chunks_after is not None else lsh_num_chunks_after
num_chunks_before = local_num_chunks_before if local_num_chunks_before is not None else lsh_num_chunks_before
self.encoder_seq_length = seq_length // attn_chunk_length + (self.seq_length % attn_chunk_length != 0)
self.key_length = (num_chunks_before + num_chunks_after + 1) * attn_chunk_length
self.chunk_length = attn_chunk_length
self.num_labels = num_labels
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
choice_labels = None
if self.use_labels:
choice_labels = ids_tensor([self.batch_size], 2)
config = self.get_config()
return (
config,
input_ids,
input_mask,
choice_labels,
)
def get_config(self):
return ReformerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
feed_forward_size=self.feed_forward_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
local_attention_probs_dropout_prob=self.local_attention_probs_dropout_prob,
lsh_attention_probs_dropout_prob=self.lsh_attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
is_decoder=self.is_decoder,
axial_pos_embds=self.axial_pos_embds,
axial_pos_shape=self.axial_pos_shape,
axial_pos_embds_dim=self.axial_pos_embds_dim,
local_attn_chunk_length=self.local_attn_chunk_length,
local_num_chunks_after=self.local_num_chunks_after,
local_num_chunks_before=self.local_num_chunks_before,
num_hashes=self.num_hashes,
num_buckets=self.num_buckets,
lsh_attn_chunk_length=self.lsh_attn_chunk_length,
lsh_num_chunks_after=self.lsh_num_chunks_after,
lsh_num_chunks_before=self.lsh_num_chunks_before,
attn_layers=self.attn_layers,
pad_token_id=self.pad_token_id,
hash_seed=self.hash_seed,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 100
config.max_position_embeddings = 100
config.axial_pos_shape = (4, 25)
config.is_decoder = False
return config
def create_and_check_reformer_model(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
# 2 * hidden_size because we use reversible resnet layers
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length, 2 * self.hidden_size)
)
def create_and_check_reformer_model_with_lm_backward(self, config, input_ids, input_mask, choice_labels):
if not self.is_training:
return
config.is_decoder = False
config.lsh_num_chunks_after = 1
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
loss = model(input_ids, attention_mask=input_mask, labels=input_ids)["loss"]
loss.backward()
def create_and_check_reformer_with_lm(self, config, input_ids, input_mask, choice_labels):
config.lsh_num_chunks_after = 0
config.is_decoder = True
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_with_mlm(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_reformer_model_with_attn_mask(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
# no special position embeddings
config.axial_pos_embds = False
config.is_decoder = is_decoder
if self.lsh_attn_chunk_length is not None:
# need to set chunk length equal sequence length to be certain that chunking works
config.lsh_attn_chunk_length = self.seq_length
model = ReformerModel(config=config)
model.to(torch_device)
model.eval()
# set all position encodings to zero so that postions don't matter
with torch.no_grad():
embedding = model.embeddings.position_embeddings.embedding
embedding.weight = nn.Parameter(torch.zeros(embedding.weight.shape).to(torch_device))
embedding.weight.requires_grad = False
half_seq_len = self.seq_length // 2
roll = self.chunk_length
half_input_ids = input_ids[:, :half_seq_len]
# normal padded
attn_mask = torch.cat(
[torch.ones_like(half_input_ids), torch.zeros_like(half_input_ids)],
dim=-1,
)
input_ids_padded = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
# shifted padded
input_ids_roll = torch.cat(
[half_input_ids, ids_tensor((self.batch_size, half_seq_len), self.vocab_size)],
dim=-1,
)
input_ids_roll = torch.roll(input_ids_roll, roll, dims=-1)
attn_mask_roll = torch.roll(attn_mask, roll, dims=-1)
output_padded = model(input_ids_padded, attention_mask=attn_mask)[0][:, :half_seq_len]
output_padded_rolled = model(input_ids_roll, attention_mask=attn_mask_roll)[0][:, roll : half_seq_len + roll]
self.parent.assertTrue(torch.allclose(output_padded, output_padded_rolled, atol=1e-3))
def create_and_check_reformer_layer_dropout_seed(
self, config, input_ids, input_mask, choice_labels, is_decoder=False
):
config.is_decoder = is_decoder
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
# get random tensors
hidden_states = floats_tensor(shape)
prev_attn_output = floats_tensor(shape)
# now the random seeds for attention and feed forward is initialized
# forward tensors with dropout
layer_outputs = layer(prev_attn_output, hidden_states, attention_mask=input_mask)
next_attn_output = layer_outputs.attn_output
next_hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
attn_outputs = layer.attention(hidden_states, attention_mask=input_mask)
self.parent.assertTrue(
torch.allclose(
prev_attn_output + attn_outputs.hidden_states,
next_attn_output,
atol=1e-3,
)
)
torch.manual_seed(layer.feed_forward_seed)
feed_forward_hidden_states = layer.feed_forward(next_attn_output)
self.parent.assertTrue(
torch.allclose(
next_hidden_states,
hidden_states + feed_forward_hidden_states,
atol=1e-3,
)
)
def create_and_check_reformer_feed_backward_chunking(self, config, input_ids, input_mask, choice_labels):
if not self.is_training:
return
# disable dropout
config.hidden_dropout_prob = 0
config.local_attention_probs_dropout_prob = 0
config.lsh_attention_probs_dropout_prob = 0
config.lsh_num_chunks_after = 1
config.is_decoder = False
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_no_chunk, output_no_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_no_chunk.backward()
grad_slice_word_no_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_no_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_no_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
config.chunk_size_lm_head = 1
config.chunk_size_feed_forward = 1
torch.manual_seed(0)
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.train()
model.zero_grad()
loss_chunk, output_chunk = model(input_ids, labels=input_ids, attention_mask=input_mask)[:2]
loss_chunk.backward()
grad_slice_word_chunk = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
grad_slice_position_factor_1_chunk = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
grad_slice_position_factor_2_chunk = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
self.parent.assertTrue(torch.allclose(loss_chunk, loss_no_chunk, atol=1e-3))
self.parent.assertTrue(torch.allclose(grad_slice_word_no_chunk, grad_slice_word_chunk, atol=1e-3))
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_1_chunk, grad_slice_position_factor_1_no_chunk, atol=1e-3)
)
self.parent.assertTrue(
torch.allclose(grad_slice_position_factor_2_chunk, grad_slice_position_factor_2_no_chunk, atol=1e-3)
)
def create_and_check_reformer_random_seed(self, config, input_ids, input_mask, choice_labels):
layer = ReformerLayer(config).to(torch_device)
layer.train()
shape = (
self.batch_size,
self.seq_length,
config.hidden_size,
) # Batch x SeqLen x hiddenSize
hidden_states = floats_tensor(shape)
attn_output = floats_tensor(shape)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.attention_seed)
seeds.append(layer.attention_seed)
self.parent.assertGreater(len(set(seeds)), 70)
seeds = []
for _ in range(100):
layer_outputs = layer(attn_output, hidden_states, attention_mask=input_mask)
attn_output = layer_outputs.attn_output
hidden_states = layer_outputs.hidden_states
torch.manual_seed(layer.feed_forward_seed)
seeds.append(layer.feed_forward_seed)
self.parent.assertGreater(len(set(seeds)), 70)
def create_and_check_reformer_model_fp16_forward(self, config, input_ids, input_mask, choice_labels):
model = ReformerModel(config=config)
model.to(torch_device)
model.half()
model.eval()
output = model(input_ids, attention_mask=input_mask)["last_hidden_state"]
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_model_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
config.bos_token_id = 0
config.eos_token_id = None
config.max_length = 20
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
output = model.generate()
self.parent.assertIsNotNone(output)
def create_and_check_reformer_model_fp16_generate(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.half()
model.eval()
# only use last 10 inputs for generation
output = model.generate(input_ids[:, -10:], attention_mask=input_mask, do_sample=False)
self.parent.assertFalse(torch.isnan(output).any().item())
def create_and_check_reformer_no_chunking(self, config, input_ids, input_mask, choice_labels):
# force chunk length to be bigger than input_ids
config.lsh_attn_chunk_length = 2 * input_ids.shape[-1]
config.local_attn_chunk_length = 2 * input_ids.shape[-1]
config.lsh_num_chunks_after = 1
config.is_decoder = False
model = ReformerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
output_logits = model(input_ids, attention_mask=input_mask)["logits"]
self.parent.assertTrue(output_logits.shape[1] == input_ids.shape[-1])
def create_and_check_reformer_for_question_answering(self, config, input_ids, input_mask, choice_labels):
model = ReformerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
start_positions=choice_labels,
end_positions=choice_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_past_buckets_states(self, config, input_ids, input_mask, choice_labels):
config.is_decoder = True
config.lsh_num_chunks_before = 1
config.lsh_num_chunks_after = 0
model = ReformerModelWithLMHead(config=config)
model.to(torch_device)
model.eval()
input_ids_first = input_ids[:, :-1]
input_ids_second = input_ids[:, -1:]
# return saved cache
past_buckets_states = model(input_ids_first, use_cache=True)["past_buckets_states"]
# calculate last output with and without cache
outputs_with_cache = model(input_ids_second, past_buckets_states=past_buckets_states, use_cache=True)["logits"]
outputs_without_cache = model(input_ids)["logits"][:, -1]
# select random slice idx
random_slice_idx = torch.randint(outputs_without_cache.shape[-1], (1, 1), device=torch_device).item()
# outputs should be similar within range
self.parent.assertTrue(
torch.allclose(
outputs_with_cache[:, 0, random_slice_idx], outputs_without_cache[:, random_slice_idx], atol=1e-2
)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
def create_and_check_reformer_for_sequence_classification(
self, config, input_ids, input_mask, choice_labels, is_decoder
):
config.is_decoder = is_decoder
sequence_labels = ids_tensor([self.batch_size], config.num_labels)
model = ReformerForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
class ReformerTesterMixin:
"""
Reformer Local and Reformer LSH run essentially the same tests
"""
def test_config(self):
self.config_tester.run_common_tests()
def test_reformer_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model(*config_and_inputs)
def test_reformer_lm_model_backward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_lm_backward(*config_and_inputs)
def test_reformer_model_attn_masking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_model_with_attn_mask(*config_and_inputs, is_decoder=False)
def test_reformer_with_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_lm(*config_and_inputs)
def test_reformer_with_mlm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_with_mlm(*config_and_inputs)
def test_reformer_layer_training_dropout(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=True)
self.model_tester.create_and_check_reformer_layer_dropout_seed(*config_and_inputs, is_decoder=False)
def test_reformer_chunking_backward_equality(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_feed_backward_chunking(*config_and_inputs)
def test_reformer_no_chunking(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_no_chunking(*config_and_inputs)
def test_reformer_qa_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_question_answering(*config_and_inputs)
def test_reformer_cached_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_past_buckets_states(*config_and_inputs)
def test_reformer_cached_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_generate(*config_and_inputs)
@slow
def test_dropout_random_seed_is_changing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_random_seed(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_reformer_model_fp16_forward(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_forward(*config_and_inputs)
@unittest.skipIf(torch_device == "cpu", "Cant do half precision")
def test_reformer_model_fp16_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_model_fp16_generate(*config_and_inputs)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"Reformer does not work with data parallel (DP) because of a bug in PyTorch:"
" https://github.com/pytorch/pytorch/issues/36035"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_reformer_for_sequence_classification(*config_and_inputs, is_decoder=False)
def test_retain_grad_hidden_states_attentions(self):
# reformer cannot keep gradients in attentions or hidden states
return
def test_resize_embeddings_untied(self):
# reformer cannot resize embeddings that easily
return
@require_torch
class ReformerLocalAttnModelTest(ReformerTesterMixin, GenerationTesterMixin, ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
test_pruning = False
test_headmasking = False
test_torchscript = False
test_sequence_classification_problem_types = True
def setUp(self):
self.model_tester = ReformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
@slow
def test_model_from_pretrained(self):
for model_name in REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ReformerModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
num_chunks = tgt_len // config.local_attn_chunk_length + (tgt_len % config.local_attn_chunk_length != 0)
tgt_chunk_len = config.local_attn_chunk_length
src_chunk_len = config.local_attn_chunk_length * (
1 + config.local_num_chunks_after + config.local_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
min_length // config.local_attn_chunk_length + 1 + idx,
)
else:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
num_chunks,
tgt_chunk_len,
src_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx
seq_len = config.local_attn_chunk_length * (
seq_len // config.local_attn_chunk_length + (seq_len % config.local_attn_chunk_length != 0)
)
if use_cache:
seq_len = 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
@require_torch
class ReformerLSHAttnModelTest(
ReformerTesterMixin, ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
):
all_model_classes = (
(ReformerModel, ReformerModelWithLMHead, ReformerForSequenceClassification, ReformerForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (ReformerModelWithLMHead,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": ReformerModel,
"fill-mask": ReformerForMaskedLM,
"question-answering": ReformerForQuestionAnswering,
"text-classification": ReformerForSequenceClassification,
"text-generation": ReformerModelWithLMHead,
"zero-shot": ReformerForSequenceClassification,
}
if is_torch_available()
else {}
)
test_pruning = False
test_headmasking = False
test_torchscript = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def setUp(self):
self.model_tester = ReformerModelTester(
self,
batch_size=13,
seq_length=13,
use_input_mask=True,
use_labels=True,
is_training=False,
is_decoder=True,
vocab_size=32,
attention_head_size=16,
hidden_size=64,
num_attention_heads=2,
num_buckets=2,
num_hashes=4,
lsh_attn_chunk_length=4,
lsh_num_chunks_before=1,
lsh_num_chunks_after=0,
chunk_size_lm_head=5,
chunk_size_feed_forward=6,
feed_forward_size=32,
hidden_act="relu",
hidden_dropout_prob=0.1,
lsh_attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
axial_norm_std=1.0,
layer_norm_eps=1e-12,
axial_pos_embds=True,
axial_pos_shape=[4, 8],
axial_pos_embds_dim=[16, 48],
# sanotheu
# attn_layers=[lsh,lsh,lsh,lsh],
attn_layers=["lsh"],
pad_token_id=0,
eos_token_id=2,
scope=None,
hash_seed=0,
num_labels=2,
)
self.config_tester = ConfigTester(self, config_class=ReformerConfig, hidden_size=37)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, list) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
tgt_len = min_length + idx if not use_cache else 1
num_chunks = tgt_len // config.lsh_attn_chunk_length + (tgt_len % config.lsh_attn_chunk_length != 0)
tgt_chunk_len = config.lsh_attn_chunk_length
src_chunk_len = config.lsh_attn_chunk_length * (
1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before
)
if use_cache:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
config.num_hashes,
tgt_len,
config.num_hashes * (1 + config.lsh_num_chunks_after + config.lsh_num_chunks_before),
)
else:
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
num_chunks * config.num_hashes,
tgt_chunk_len,
src_chunk_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, list) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
seq_len = min_length + idx if not use_cache else 1
seq_len = config.lsh_attn_chunk_length * (
seq_len // config.lsh_attn_chunk_length + (seq_len % config.lsh_attn_chunk_length != 0)
)
if use_cache:
seq_len = 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
@unittest.skip("Fails because the sequence length is not a multiple of 4")
def test_problem_types(self):
pass
@unittest.skip("Fails because the sequence length is not a multiple of 4")
def test_past_key_values_format(self):
pass
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class ReformerIntegrationTests(unittest.TestCase):
"""
These integration tests test the current layer activations and gradients againts the output of the Hugging Face Reformer model at time of integration: 29/06/2020. During integration, the model was tested against the output of the official Trax ReformerLM model for various cases ("lsh" only, "lsh" only, masked / non-masked, different chunk length, ....). In order to recover the original trax integration tests, one should use patrickvonplaten's fork of trax and the code that lives on the branch `reformer_trax_tests`.
"""
def _get_basic_config_and_input(self):
config = {
"vocab_size": 320,
"attention_head_size": 8,
"hidden_size": 16,
"num_attention_heads": 2,
"num_buckets": 2,
"num_hashes": 4,
"lsh_attn_chunk_length": 4,
"local_attn_chunk_length": 4,
"lsh_num_chunks_before": 1,
"lsh_num_chunks_after": 0,
"local_num_chunks_before": 1,
"local_num_chunks_after": 0,
"chunk_size_lm_head": 0,
"chunk_size_feed_forward": 0,
"feed_forward_size": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"lsh_attention_probs_dropout_prob": 0.0,
"local_attention_probs_dropout_prob": 0.0,
"max_position_embeddings": 32,
"initializer_range": 0.02,
"axial_norm_std": 1.0,
"layer_norm_eps": 1e-12,
"sinusoidal_pos_embds": False,
"axial_pos_embds": True,
"axial_pos_shape": [4, 8],
"axial_pos_embds_dim": [8, 8],
"hash_seed": 0,
"is_decoder": True,
}
return config
def _get_hidden_states(self):
return torch.tensor(
[
[
[
1.90826353e00,
-1.45999730e00,
-6.20405462e-01,
1.52503433e00,
-3.64464232e-01,
-8.27359235e-01,
8.39670803e-01,
2.44492178e-01,
4.98332758e-01,
2.69175139e00,
-7.08081422e-03,
1.04915401e00,
-1.83476661e00,
7.67220476e-01,
2.98580543e-01,
2.84803992e-02,
],
[
-2.66374286e-02,
4.33497576e-01,
3.10386309e-01,
5.46039944e-01,
-2.47292666e-04,
-7.52305019e-01,
2.39162103e-01,
7.25216186e-01,
-7.58357372e-01,
4.20635998e-01,
-4.04739919e-02,
1.59924145e-01,
2.05135748e00,
-1.15997978e00,
5.37166397e-01,
2.62873606e-01,
],
[
1.85247482e-01,
7.07046037e-01,
-6.77089715e-01,
-2.24209655e00,
-3.75307980e-02,
-8.59380874e-01,
-2.81027884e00,
1.01276376e00,
-1.69438001e00,
4.17574660e-01,
-1.49196962e00,
-1.76483717e00,
-1.94566312e-01,
-1.71183858e00,
7.72903565e-01,
-1.11557056e00,
],
[
9.46069193e-01,
1.53417623e-01,
-9.58686996e-01,
1.18126669e-01,
1.75967724e00,
1.62194590e00,
-5.74108159e-01,
6.79920443e-01,
5.44028163e-01,
2.05466114e-01,
-3.63045868e-01,
2.41865062e-01,
3.20348382e-01,
-9.05611176e-01,
-1.92690727e-01,
-1.19917547e00,
],
]
],
dtype=torch.float32,
device=torch_device,
)
def _get_attn_mask(self):
return torch.tensor([[0, 1, 0, 0]], dtype=torch.long, device=torch_device)
def _get_input_ids_and_mask(self):
mask = torch.tensor(
[
[1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0],
],
dtype=torch.long,
device=torch_device,
)
input_ids = torch.tensor(
[
[
89,
279,
286,
84,
194,
316,
182,
28,
283,
37,
169,
7,
253,
267,
107,
250,
44,
7,
102,
62,
3,
243,
171,
265,
302,
48,
164,
264,
148,
229,
280,
150,
],
[
9,
192,
66,
112,
163,
83,
135,
70,
224,
96,
31,
80,
196,
80,
63,
22,
85,
100,
47,
283,
0,
163,
126,
143,
195,
82,
53,
82,
18,
27,
182,
52,
],
],
dtype=torch.long,
device=torch_device,
)
return input_ids, mask
def test_lsh_layer_forward(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states.clone(), hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6879, -1.3083, -0.4708, 1.3555, -0.6292],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lsh_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["lsh_num_chunks_before"] = 0
config["attn_layers"] = ["lsh"]
config["num_buckets"] = [2, 4]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states.clone(),
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.6439, -1.2306, -0.5108, 1.3006, -0.6537],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_layer_forward(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
config["is_decoder"] = False
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(prev_attn_output=hidden_states, hidden_states=hidden_states)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4212, -2.0576, -0.9688, 1.4599, -0.1344],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_layer_forward_complex(self):
config = self._get_basic_config_and_input()
config["local_num_chunks_before"] = 0
config["attn_layers"] = ["local"]
attn_mask = self._get_attn_mask()
hidden_states = self._get_hidden_states()
torch.manual_seed(0)
layer = ReformerLayer(ReformerConfig(**config)).to(torch_device)
layer.eval()
reformer_output = layer(
prev_attn_output=hidden_states,
hidden_states=hidden_states,
attention_mask=attn_mask,
)
output_slice = reformer_output.hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[1.4750, -2.0235, -0.9743, 1.4463, -0.1269],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lsh_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["num_buckets"] = [2, 4]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-0.9896, -0.9396, -1.0831, -0.0597, 0.2456],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
torch.manual_seed(0)
model = ReformerModel(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[0, 0, :5]
expected_output_slice = torch.tensor(
[-1.6791, 0.7171, 0.1594, 0.4063, 1.2584],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_lm_model_forward(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "lsh", "local", "lsh", "local", "lsh"]
config["num_buckets"] = [2, 4]
config["is_decoder"] = False
torch.manual_seed(0)
model = ReformerForMaskedLM(ReformerConfig(**config)).to(torch_device)
model.eval()
input_ids, attn_mask = self._get_input_ids_and_mask()
hidden_states = model(input_ids=input_ids, attention_mask=attn_mask)[0]
output_slice = hidden_states[1, -1, :5]
expected_output_slice = torch.tensor(
[0.1018, -0.2026, 0.2116, 0.0270, -0.1233],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_local_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["local", "local", "local", "local"]
config["hidden_dropout_prob"] = 0.0
config["local_attention_probs_dropout_prob"] = 0.0
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.8019, dtype=torch.float, device=torch_device), atol=1e-3))
loss.backward()
# check last grads to cover all proable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[-0.0005, -0.0001, -0.0002, -0.0006, -0.0006],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[-0.5235, 0.5704, 0.0922, -0.3140, 0.9928],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[1.7960, 1.7668, 0.5593, 0.0907, 1.8342],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
def test_lsh_lm_model_grad(self):
config = self._get_basic_config_and_input()
config["attn_layers"] = ["lsh", "lsh", "lsh", "lsh"]
config["hidden_dropout_prob"] = 0.0
config["lsh_attention_probs_dropout_prob"] = 0.0
config["num_buckets"] = [2, 4]
config["num_hashes"] = 6
torch.manual_seed(0)
model = ReformerModelWithLMHead(ReformerConfig(**config)).to(torch_device)
model.train()
model.zero_grad()
input_ids, _ = self._get_input_ids_and_mask()
loss = model(input_ids=input_ids, labels=input_ids)[0]
self.assertTrue(torch.allclose(loss, torch.tensor(5.7854, dtype=torch.float, device=torch_device), atol=1e-3))
loss.backward()
# check last grads to cover all proable errors
grad_slice_word = model.reformer.embeddings.word_embeddings.weight.grad[0, :5]
expected_grad_slice_word = torch.tensor(
[0.0004, 0.0003, 0.0006, -0.0004, 0.0002],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_1 = model.reformer.embeddings.position_embeddings.weights[0][1, 0, -5:]
expected_grad_slice_pos_fac_1 = torch.tensor(
[-0.3792, 0.5593, -1.6993, 0.2033, 0.4131],
dtype=torch.float,
device=torch_device,
)
grad_slice_position_factor_2 = model.reformer.embeddings.position_embeddings.weights[1][0, 1, :5]
expected_grad_slice_pos_fac_2 = torch.tensor(
[-1.4212, -0.3201, -1.1944, 0.1258, 0.2856],
dtype=torch.float,
device=torch_device,
)
self.assertTrue(torch.allclose(grad_slice_word, expected_grad_slice_word, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_1, expected_grad_slice_pos_fac_1, atol=1e-3))
self.assertTrue(torch.allclose(grad_slice_position_factor_2, expected_grad_slice_pos_fac_2, atol=1e-3))
@slow
def test_pretrained_generate_crime_and_punish(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids = model.generate(
input_ids, max_length=50, num_beams=4, early_stopping=True, do_sample=False, num_hashes=8
)
output = tokenizer.decode(output_ids[0])
self.assertEqual(
output,
"A few months later state expression in his ideas, at the first entrance. He was positively for an inst",
)
@slow
def test_pretrained_generate_use_cache_equality(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids_with_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=False)
output_ids_without_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=True)
output_with_cache = tokenizer.decode(output_ids_with_cache[0])
output_without_cache = tokenizer.decode(output_ids_without_cache[0])
self.assertEqual(output_with_cache, output_without_cache)
| 53,865 | 40.276628 | 524 | py |
transformers | transformers-main/tests/models/megatron_bert/test_modeling_megatron_bert.py | # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
# Copyright 2021 NVIDIA Corporation. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MegatronBERT model. """
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class MegatronBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=64,
embedding_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.embedding_size = embedding_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return MegatronBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
embedding_size=self.embedding_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def create_and_check_megatron_bert_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_megatron_bert_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_causal_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_megatron_bert_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_megatron_bert_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_megatron_bert_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = MegatronBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_megatron_bert_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MegatronBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_megatron_bert_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = MegatronBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_megatron_bert_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = MegatronBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class MegatronBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": MegatronBertModel,
"fill-mask": MegatronBertForMaskedLM,
"question-answering": MegatronBertForQuestionAnswering,
"text-classification": MegatronBertForSequenceClassification,
"text-generation": MegatronBertForCausalLM,
"token-classification": MegatronBertForTokenClassification,
"zero-shot": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
# test_resize_embeddings = False
test_head_masking = False
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = MegatronBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=MegatronBertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_megatron_bert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*config_and_inputs)
def _long_tensor(tok_lst):
return torch.tensor(
tok_lst,
dtype=torch.long,
device=torch_device,
)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class MegatronBertModelIntegrationTests(unittest.TestCase):
@slow
@unittest.skip("Model is not available.")
def test_inference_no_head(self):
directory = "nvidia/megatron-bert-uncased-345m"
if "MYDIR" in os.environ:
directory = os.path.join(os.environ["MYDIR"], directory)
model = MegatronBertModel.from_pretrained(directory)
model.to(torch_device)
model.half()
input_ids = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 9, 1024))
self.assertEqual(output.shape, expected_shape)
expected = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3):
for jj in range(3):
a = output[0, ii, jj]
b = expected[3 * ii + jj]
msg = "ii={} jj={} a={} b={}".format(ii, jj, a, b)
self.assertTrue(math.isclose(a, b, rel_tol=TOLERANCE, abs_tol=TOLERANCE), msg=msg)
| 16,500 | 40.880711 | 119 | py |
transformers | transformers-main/tests/models/blip_2/test_processor_blip_2.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Blip2Processor, BlipImageProcessor, GPT2Tokenizer, PreTrainedTokenizerFast
@require_vision
class Blip2ProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
image_processor = BlipImageProcessor()
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
processor = Blip2Processor(image_processor, tokenizer)
processor.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_additional_features(self):
processor = Blip2Processor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = Blip2Processor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, BlipImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_feat_extract = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str, return_token_type_ids=False)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
| 5,753 | 36.855263 | 118 | py |
transformers | transformers-main/tests/models/blip_2/test_modeling_blip_2.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch BLIP-2 model. """
import inspect
import tempfile
import unittest
import numpy as np
import requests
from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import Blip2ForConditionalGeneration, Blip2Model, Blip2VisionModel
from transformers.models.blip_2.modeling_blip_2 import BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import Blip2Processor
class Blip2VisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=30,
patch_size=2,
num_channels=3,
is_training=True,
hidden_size=32,
projection_dim=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=1e-10,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return Blip2VisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = Blip2VisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class Blip2VisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as BLIP-2's vision encoder does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (Blip2VisionModel,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = Blip2VisionModelTester(self)
self.config_tester = ConfigTester(
self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="BLIP-2's vision encoder does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = Blip2VisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class Blip2QFormerModelTester:
def __init__(
self,
parent,
batch_size=12,
seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
projection_dim=32,
num_hidden_layers=6,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=512,
initializer_range=0.02,
bos_token_id=0,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
if input_mask is not None:
batch_size, seq_length = input_mask.shape
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
input_mask[batch_idx, :start_index] = 1
input_mask[batch_idx, start_index:] = 0
config = self.get_config()
return config, input_ids, input_mask
def get_config(self):
return Blip2QFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
)
# this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py
class Blip2TextModelDecoderOnlyTester:
def __init__(
self,
parent,
batch_size=12,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
embed_dim=16,
num_labels=3,
word_embed_proj_dim=16,
type_sequence_label_size=2,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.embed_dim = embed_dim
self.num_labels = num_labels
self.type_sequence_label_size = type_sequence_label_size
self.word_embed_proj_dim = word_embed_proj_dim
self.is_encoder_decoder = False
def prepare_config_and_inputs(self):
config = self.get_config()
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
input_ids[:, -1] = self.eos_token_id # Eos Token
attention_mask = input_ids.ne(self.pad_token_id)
return config, input_ids, attention_mask
def get_config(self):
return CONFIG_MAPPING["opt"](
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
embed_dim=self.embed_dim,
is_encoder_decoder=False,
word_embed_proj_dim=self.word_embed_proj_dim,
)
# this model tester uses a decoder-only language model (OPT)
class Blip2ForConditionalGenerationDecoderOnlyModelTester:
def __init__(
self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
):
if vision_kwargs is None:
vision_kwargs = {}
if qformer_kwargs is None:
qformer_kwargs = {}
if text_kwargs is None:
text_kwargs = {}
self.parent = parent
self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
self.text_model_tester = Blip2TextModelDecoderOnlyTester(parent, **text_kwargs)
self.is_training = is_training
self.num_query_tokens = num_query_tokens
def prepare_config_and_inputs(self):
_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
_, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
config = self.get_config()
return config, input_ids, attention_mask, pixel_values
def get_config(self):
return Blip2Config.from_vision_qformer_text_configs(
vision_config=self.vision_model_tester.get_config(),
qformer_config=self.qformer_model_tester.get_config(),
text_config=self.text_model_tester.get_config(),
num_query_tokens=self.num_query_tokens,
)
def create_and_check_for_conditional_generation(self, config, input_ids, attention_mask, pixel_values):
model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
with torch.no_grad():
result = model(pixel_values, input_ids, attention_mask)
expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length
self.parent.assertEqual(
result.logits.shape,
(self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_size),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask, pixel_values = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": input_ids,
}
return config, inputs_dict
@require_torch
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
fx_compatible = False
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
test_torchscript = False
def setUp(self):
self.model_tester = Blip2ForConditionalGenerationDecoderOnlyModelTester(self)
def test_for_conditional_generation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="Blip2Model does not have input/output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="There's no base Blip2Model")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="There's no base Blip2Model")
def test_save_load_fast_init_to_base(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_load_vision_qformer_text_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Save Blip2Config and check if we can load Blip2VisionConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
# Save Blip2Config and check if we can load Blip2QFormerConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())
@slow
def test_model_from_pretrained(self):
for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
model = Blip2ForConditionalGeneration.from_pretrained(model_name)
self.assertIsNotNone(model)
# this class is based on `T5ModelTester` found in tests/models/t5/test_modeling_t5.py
class Blip2TextModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=12,
encoder_seq_length=7,
decoder_seq_length=9,
# For common tests
is_training=True,
use_attention_mask=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
d_ff=37,
relative_attention_num_buckets=8,
dropout_rate=0.1,
initializer_factor=0.002,
eos_token_id=1,
pad_token_id=0,
decoder_start_token_id=0,
scope=None,
decoder_layers=None,
):
self.parent = parent
self.batch_size = batch_size
self.encoder_seq_length = encoder_seq_length
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.d_ff = d_ff
self.relative_attention_num_buckets = relative_attention_num_buckets
self.dropout_rate = dropout_rate
self.initializer_factor = initializer_factor
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.scope = None
self.decoder_layers = decoder_layers
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
decoder_attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = self.get_config()
return (
config,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
)
def get_config(self):
return CONFIG_MAPPING["t5"](
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.d_ff,
d_kv=self.hidden_size // self.num_attention_heads,
num_layers=self.num_hidden_layers,
num_decoder_layers=self.decoder_layers,
num_heads=self.num_attention_heads,
relative_attention_num_buckets=self.relative_attention_num_buckets,
dropout_rate=self.dropout_rate,
initializer_factor=self.initializer_factor,
eos_token_id=self.eos_token_id,
bos_token_id=self.pad_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
)
# this model tester uses an encoder-decoder language model (T5)
class Blip2ModelTester:
def __init__(
self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
):
if vision_kwargs is None:
vision_kwargs = {}
if qformer_kwargs is None:
qformer_kwargs = {}
if text_kwargs is None:
text_kwargs = {}
self.parent = parent
self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
self.text_model_tester = Blip2TextModelTester(parent, **text_kwargs)
self.is_training = is_training
self.num_query_tokens = num_query_tokens
def prepare_config_and_inputs(self):
_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
(
_,
input_ids,
decoder_input_ids,
attention_mask,
decoder_attention_mask,
lm_labels,
) = self.text_model_tester.prepare_config_and_inputs()
config = self.get_config()
return config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, lm_labels
def get_config(self):
return Blip2Config.from_vision_qformer_text_configs(
vision_config=self.vision_model_tester.get_config(),
qformer_config=self.qformer_model_tester.get_config(),
text_config=self.text_model_tester.get_config(),
num_query_tokens=self.num_query_tokens,
)
def create_and_check_for_conditional_generation(
self, config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, labels
):
model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
with torch.no_grad():
result = model(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
self.parent.assertEqual(
result.logits.shape,
(
self.vision_model_tester.batch_size,
self.text_model_tester.seq_length,
self.text_model_tester.vocab_size,
),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
pixel_values,
decoder_input_ids,
decoder_attention_mask,
labels,
) = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"labels": labels,
}
return config, inputs_dict
@require_torch
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": Blip2Model, "image-to-text": Blip2ForConditionalGeneration}
if is_torch_available()
else {}
)
fx_compatible = False
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
test_torchscript = False
def setUp(self):
self.model_tester = Blip2ModelTester(self)
def test_for_conditional_generation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs)
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="Blip2Model does not have input/output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="There's no base Blip2Model")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="There's no base Blip2Model")
def test_save_load_fast_init_to_base(self):
pass
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
def test_cpu_offload(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_load_vision_qformer_text_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Save Blip2Config and check if we can load Blip2VisionConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
# Save Blip2Config and check if we can load Blip2QFormerConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())
@slow
def test_model_from_pretrained(self):
for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
model = Blip2ForConditionalGeneration.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_get_text_features(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
inputs_dict = {
"input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
"attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(torch_device),
"decoder_input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device),
}
model = Blip2Model(config).to(torch_device)
model.eval()
text_features = model.get_text_features(**inputs_dict)
self.assertEqual(text_features[0].shape, (1, 10, config.text_config.vocab_size))
def test_get_image_features(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]
for key in keys_to_pop:
inputs_dict.pop(key)
model = Blip2Model(config).to(torch_device)
model.eval()
image_features = model.get_image_features(**inputs_dict)
self.assertEqual(
image_features[0].shape,
(
self.model_tester.vision_model_tester.batch_size,
self.model_tester.vision_model_tester.seq_length,
config.vision_config.hidden_size,
),
)
def test_get_qformer_features(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]
for key in keys_to_pop:
inputs_dict.pop(key)
model = Blip2Model(config).to(torch_device)
model.eval()
qformer_features = model.get_qformer_features(**inputs_dict)
self.assertEqual(
qformer_features[0].shape,
(self.model_tester.vision_model_tester.batch_size, 10, config.vision_config.hidden_size),
)
# override from common to deal with nested configurations (`vision_config`, `text_config` and `qformer_config`)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for key in ["vision_config", "qformer_config", "text_config"]:
setattr(configs_no_init, key, _config_zero_init(getattr(configs_no_init, key)))
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# We will verify our results on an image of cute cats
def prepare_img():
url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)
return image
@require_vision
@require_torch
@slow
class Blip2ModelIntegrationTest(unittest.TestCase):
def test_inference_opt(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
self.assertEqual("a woman sitting on the beach with a dog", generated_text)
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(
predictions[0].tolist(),
[2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
)
self.assertEqual(generated_text, "it's not a city, it's a beach")
def test_inference_opt_batched_beam_search(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs, num_beams=2)
# Test output (in this case, slightly different from greedy search)
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
self.assertEqual(predictions[1].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
def test_inference_t5(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
self.assertEqual("woman playing with dog on the beach", generated_text)
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(
predictions[0].tolist(),
[0, 3, 7, 152, 67, 839, 1],
)
self.assertEqual(generated_text, "san diego")
def test_inference_t5_batched_beam_search(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16
).to(torch_device)
# prepare image
image = prepare_img()
inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16)
predictions = model.generate(**inputs, num_beams=2)
# Test output (in this case, slightly different from greedy search)
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
self.assertEqual(predictions[1].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
@require_torch_multi_gpu
def test_inference_opt_multi_gpu(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="balanced"
)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
self.assertEqual("a woman sitting on the beach with a dog", generated_text)
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(
predictions[0].tolist(),
[2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
)
self.assertEqual(generated_text, "it's not a city, it's a beach")
@require_torch_multi_gpu
def test_inference_t5_multi_gpu(self):
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
device_map = device_map = {
"query_tokens": 0,
"vision_model": 0,
"language_model": 1,
"language_projection": 0,
"qformer": 0,
}
model = Blip2ForConditionalGeneration.from_pretrained(
"Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16, device_map=device_map
)
# prepare image
image = prepare_img()
inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
self.assertEqual("woman playing with dog on the beach", generated_text)
# image and context
prompt = "Question: which city is this? Answer:"
inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16)
predictions = model.generate(**inputs)
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
# Test output
self.assertEqual(
predictions[0].tolist(),
[0, 3, 7, 152, 67, 839, 1],
)
self.assertEqual(generated_text, "san diego")
| 38,602 | 37.914315 | 138 | py |
transformers | transformers-main/tests/models/imagegpt/test_image_processing_imagegpt.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class ImageGPTImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
def prepare_image_processor_dict(self):
return {
# here we create 2 clusters for the sake of simplicity
"clusters": np.asarray(
[
[0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
[-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
]
),
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
}
@require_torch
@require_vision
class ImageGPTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = ImageGPTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = ImageGPTImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "clusters"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_image_processor_to_json_string(self):
image_processor = self.image_processing_class(**self.image_processor_dict)
obj = json.loads(image_processor.to_json_string())
for key, value in self.image_processor_dict.items():
if key == "clusters":
self.assertTrue(np.array_equal(value, obj[key]))
else:
self.assertEqual(obj[key], value)
def test_image_processor_to_json_file(self):
image_processor_first = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
json_file_path = os.path.join(tmpdirname, "image_processor.json")
image_processor_first.to_json_file(json_file_path)
image_processor_second = self.image_processing_class.from_json_file(json_file_path).to_dict()
image_processor_first = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(value, image_processor_second[key]))
else:
self.assertEqual(image_processor_first[key], value)
def test_image_processor_from_and_save_pretrained(self):
image_processor_first = self.image_processing_class(**self.image_processor_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
image_processor_first.save_pretrained(tmpdirname)
image_processor_second = self.image_processing_class.from_pretrained(tmpdirname).to_dict()
image_processor_first = image_processor_first.to_dict()
for key, value in image_processor_first.items():
if key == "clusters":
self.assertTrue(np.array_equal(value, image_processor_second[key]))
else:
self.assertEqual(image_processor_first[key], value)
@unittest.skip("ImageGPT requires clusters at initialization")
def test_init_without_params(self):
pass
def prepare_images():
dataset = load_dataset("hf-internal-testing/fixtures_image_utils", split="test")
image1 = Image.open(dataset[4]["file"])
image2 = Image.open(dataset[5]["file"])
images = [image1, image2]
return images
@require_vision
@require_torch
class ImageGPTImageProcessorIntegrationTest(unittest.TestCase):
@slow
def test_image(self):
image_processing = ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")
images = prepare_images()
# test non-batched
encoding = image_processing(images[0], return_tensors="pt")
self.assertIsInstance(encoding.input_ids, torch.LongTensor)
self.assertEqual(encoding.input_ids.shape, (1, 1024))
expected_slice = [306, 191, 191]
self.assertEqual(encoding.input_ids[0, :3].tolist(), expected_slice)
# test batched
encoding = image_processing(images, return_tensors="pt")
self.assertIsInstance(encoding.input_ids, torch.LongTensor)
self.assertEqual(encoding.input_ids.shape, (2, 1024))
expected_slice = [303, 13, 13]
self.assertEqual(encoding.input_ids[1, -3:].tolist(), expected_slice)
| 6,726 | 35.362162 | 105 | py |
transformers | transformers-main/tests/models/imagegpt/test_modeling_imagegpt.py | # coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import inspect
import os
import tempfile
import unittest
from transformers import ImageGPTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
ImageGPTForCausalImageModeling,
ImageGPTForImageClassification,
ImageGPTModel,
)
if is_vision_available():
from PIL import Image
from transformers import ImageGPTImageProcessor
class ImageGPTModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
def get_large_model_config(self):
return ImageGPTConfig.from_pretrained("imagegpt")
def prepare_config_and_inputs(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
pixel_values = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config(
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
pixel_values,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(
self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
):
return ImageGPTConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
n_inner=self.intermediate_size,
activation_function=self.hidden_act,
resid_pdrop=self.hidden_dropout_prob,
attn_pdrop=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
use_cache=True,
gradient_checkpointing=gradient_checkpointing,
scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
reorder_and_upcast_attn=reorder_and_upcast_attn,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 513
config.max_position_embeddings = 1024
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
pixel_values,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
pixel_values,
input_mask,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_imagegpt_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
model = ImageGPTModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(pixel_values, token_type_ids=token_type_ids)
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
model = ImageGPTForCausalImageModeling(config)
model.to(torch_device)
model.eval()
labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
result = model(pixel_values, token_type_ids=token_type_ids, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
# ImageGPTForCausalImageModeling doens't have tied input- and output embeddings
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size - 1))
def create_and_check_imagegpt_for_image_classification(
self, config, pixel_values, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args
):
config.num_labels = self.num_labels
model = ImageGPTForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
pixel_values,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
return config, inputs_dict
@require_torch
class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel) if is_torch_available() else ()
)
all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
if is_torch_available()
else {}
)
test_missing_keys = False
input_name = "pixel_values"
# as ImageGPTForImageClassification isn't included in any auto mapping, we add labels here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "ImageGPTForImageClassification":
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
# we overwrite the _check_scores method of GenerationTesterMixin, as ImageGPTForCausalImageModeling doesn't have tied input- and output embeddings
def _check_scores(self, batch_size, scores, length, config):
expected_shape = (batch_size, config.vocab_size - 1)
self.assertIsInstance(scores, tuple)
self.assertEqual(len(scores), length)
self.assertListEqual([iter_scores.shape for iter_scores in scores], [expected_shape] * len(scores))
def setUp(self):
self.model_tester = ImageGPTModelTester(self)
self.config_tester = ConfigTester(self, config_class=ImageGPTConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_imagegpt_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_imagegpt_model(*config_and_inputs)
def test_imagegpt_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_imagegpt_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_imagegpt_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ImageGPTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_ids"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_resize_tokens_embeddings(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
(
original_config,
inputs_dict,
) = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds = model.get_output_embeddings()
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["pixel_values"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
pixel_values = inputs["pixel_values"]
del inputs["pixel_values"]
wte = model.get_input_embeddings()
inputs["inputs_embeds"] = wte(pixel_values)
with torch.no_grad():
model(**inputs)[0]
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class)
try:
pixel_values = inputs["pixel_values"]
traced_model = torch.jit.trace(model, pixel_values)
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
model.to(torch_device)
model.eval()
loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
if layer_name in loaded_model_state_dict:
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ImageGPTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small") if is_vision_available() else None
@slow
def test_inference_causal_lm_head(self):
model = ImageGPTForCausalImageModeling.from_pretrained("openai/imagegpt-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1024, 512))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor(
[[2.3445, 2.6889, 2.7313], [1.0530, 1.2416, 0.5699], [0.2205, 0.7749, 0.3953]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
| 22,928 | 39.015707 | 150 | py |
transformers | transformers-main/tests/models/yolos/test_image_processing_yolos.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class YolosImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
do_rescale=True,
rescale_factor=1 / 255,
do_pad=True,
):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_pad = do_pad
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to YolosImageProcessor,
assuming do_resize is set to True with a scalar size.
"""
if not batched:
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
else:
h, w = image.shape[1], image.shape[2]
if w < h:
expected_height = int(self.size["shortest_edge"] * h / w)
expected_width = self.size["shortest_edge"]
elif w > h:
expected_height = self.size["shortest_edge"]
expected_width = int(self.size["shortest_edge"] * w / h)
else:
expected_height = self.size["shortest_edge"]
expected_width = self.size["shortest_edge"]
else:
expected_values = []
for image in image_inputs:
expected_height, expected_width = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
expected_height = max(expected_values, key=lambda item: item[0])[0]
expected_width = max(expected_values, key=lambda item: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class YolosImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = YolosImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = YolosImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad, True)
image_processor = self.image_processing_class.from_dict(
self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False
)
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, False)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_equivalence_padding(self):
# Initialize image_processings
image_processing_1 = self.image_processing_class(**self.image_processor_dict)
image_processing_2 = self.image_processing_class(do_resize=False, do_normalize=False, do_rescale=False)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test whether the method "pad" and calling the image processor return the same tensors
encoded_images_with_method = image_processing_1.pad(image_inputs, return_tensors="pt")
encoded_images = image_processing_2(image_inputs, return_tensors="pt")
self.assertTrue(
torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4)
)
@slow
def test_call_pytorch_with_coco_detection_annotations(self):
# prepare image and target
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
target = json.loads(f.read())
target = {"image_id": 39769, "annotations": target}
# encode them
image_processing = YolosImageProcessor.from_pretrained("hustvl/yolos-small")
encoding = image_processing(images=image, annotations=target, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
@slow
def test_call_pytorch_with_coco_panoptic_annotations(self):
# prepare image, target and masks_path
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
target = json.loads(f.read())
target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}
masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
# encode them
image_processing = YolosImageProcessor(format="coco_panoptic")
encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt")
# verify pixel values
expected_shape = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, expected_shape)
expected_slice = torch.tensor([0.2796, 0.3138, 0.3481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4))
# verify area
expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area))
# verify boxes
expected_boxes_shape = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape)
expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3))
# verify image_id
expected_image_id = torch.tensor([39769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id))
# verify is_crowd
expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd))
# verify class_labels
expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels))
# verify masks
expected_masks_sum = 822873
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum)
# verify orig_size
expected_orig_size = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size))
# verify size
expected_size = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size))
| 15,320 | 42.036517 | 117 | py |
transformers | transformers-main/tests/models/yolos/test_modeling_yolos.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch YOLOS model. """
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class YolosModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=[30, 30],
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
num_labels=3,
scope=None,
n_targets=8,
num_detection_tokens=10,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.n_targets = n_targets
self.num_detection_tokens = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
num_patches = (image_size[1] // patch_size) * (image_size[0] // patch_size)
self.expected_seq_len = num_patches + 1 + self.num_detection_tokens
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
labels.append(target)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return YolosConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
num_detection_tokens=self.num_detection_tokens,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = YolosModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
)
def create_and_check_for_object_detection(self, config, pixel_values, labels):
model = YolosForObjectDetection(config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
result = model(pixel_values=pixel_values, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as YOLOS does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_torchscript = False
# special case for head model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = YolosModelTester(self)
self.config_tester = ConfigTester(self, config_class=YolosConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_inputs_embeds(self):
# YOLOS does not use inputs_embeds
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
# in YOLOS, the seq_len is different
seq_len = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# YOLOS has a different seq_length
seq_length = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_object_detection(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = YolosModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class YolosModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None
@slow
def test_inference_object_detection_head(self):
model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(inputs.pixel_values)
# verify outputs
expected_shape = torch.Size((1, 100, 92))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice_logits = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]],
device=torch_device,
)
expected_slice_boxes = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice_logits, atol=1e-4))
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
# verify postprocessing
results = image_processor.post_process_object_detection(
outputs, threshold=0.3, target_sizes=[image.size[::-1]]
)[0]
expected_scores = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(torch_device)
expected_labels = [75, 75, 17, 63, 17]
expected_slice_boxes = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(torch_device)
self.assertEqual(len(results["scores"]), 5)
self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4))
self.assertSequenceEqual(results["labels"].tolist(), expected_labels)
self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes))
| 15,700 | 39.362468 | 120 | py |
transformers | transformers-main/tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
global_rng = random.Random()
if is_torch_available():
import torch
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class ASTFeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=1,
padding_value=0.0,
sampling_rate=16000,
return_attention_mask=True,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
speech_inputs = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class ASTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = ASTFeatureExtractor
def setUp(self):
self.feat_extract_tester = ASTFeatureExtractionTester(self)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test not batched input
encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feat_extract(speech_inputs, padding=True, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, padding=True, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
@require_torch
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_values.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_values.dtype == torch.float32)
def _load_datasamples(self, num_samples):
from datasets import load_dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
def test_integration(self):
# fmt: off
EXPECTED_INPUT_VALUES = torch.tensor(
[-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
-1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
-1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
-0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]
)
# fmt: on
input_speech = self._load_datasamples(1)
feature_extractor = ASTFeatureExtractor()
input_values = feature_extractor(input_speech, return_tensors="pt").input_values
self.assertEquals(input_values.shape, (1, 1024, 128))
self.assertTrue(torch.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))
| 6,997 | 38.988571 | 113 | py |
transformers | transformers-main/tests/models/audio_spectrogram_transformer/test_modeling_audio_spectrogram_transformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Audio Spectrogram Transformer (AST) model. """
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class ASTModelTester:
def __init__(
self,
parent,
batch_size=13,
patch_size=2,
max_length=24,
num_mel_bins=16,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
scope=None,
frequency_stride=2,
time_stride=2,
):
self.parent = parent
self.batch_size = batch_size
self.patch_size = patch_size
self.max_length = max_length
self.num_mel_bins = num_mel_bins
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
self.frequency_stride = frequency_stride
self.time_stride = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
frequency_out_dimension = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
time_out_dimension = (self.max_length - self.patch_size) // self.time_stride + 1
num_patches = frequency_out_dimension * time_out_dimension
self.seq_length = num_patches + 2
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, input_values, labels
def get_config(self):
return ASTConfig(
patch_size=self.patch_size,
max_length=self.max_length,
num_mel_bins=self.num_mel_bins,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
frequency_stride=self.frequency_stride,
time_stride=self.time_stride,
)
def create_and_check_model(self, config, input_values, labels):
model = ASTModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_values,
labels,
) = config_and_inputs
inputs_dict = {"input_values": input_values}
return config, inputs_dict
@require_torch
class ASTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as AST does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
# TODO: Fix the failed tests when this model gets more usage
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def setUp(self):
self.model_tester = ASTModelTester(self)
self.config_tester = ConfigTester(self, config_class=ASTConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="AST does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ASTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on some audio from AudioSet
def prepare_audio():
filepath = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint", filename="sample_audio.flac", repo_type="dataset"
)
audio, sampling_rate = torchaudio.load(filepath)
return audio, sampling_rate
@require_torch
@require_torchaudio
class ASTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_feature_extractor(self):
return (
ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")
if is_torchaudio_available()
else None
)
@slow
def test_inference_audio_classification(self):
feature_extractor = self.default_feature_extractor
model = ASTForAudioClassification.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593").to(torch_device)
feature_extractor = self.default_feature_extractor
audio, sampling_rate = prepare_audio()
audio = audio.squeeze().numpy()
inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 527))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.8760, -7.0042, -8.6602]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
| 9,495 | 35.106464 | 118 | py |
transformers | transformers-main/tests/models/funnel/test_modeling_funnel.py | # coding=utf-8
# Copyright 2020 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FunnelBaseModel,
FunnelForMaskedLM,
FunnelForMultipleChoice,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForSequenceClassification,
FunnelForTokenClassification,
FunnelModel,
)
class FunnelModelTester:
"""You can also import this e.g, from .test_modeling_funnel import FunnelModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
block_sizes=[1, 1, 2],
num_decoder_layers=1,
d_model=32,
n_head=4,
d_head=8,
d_inner=37,
hidden_act="gelu_new",
hidden_dropout=0.1,
attention_dropout=0.1,
activation_dropout=0.0,
max_position_embeddings=512,
type_vocab_size=3,
initializer_std=0.02, # Set to a smaller value, so we can keep the small error threshold (1e-5) in the test
num_labels=3,
num_choices=4,
scope=None,
base=False,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.block_sizes = block_sizes
self.num_decoder_layers = num_decoder_layers
self.d_model = d_model
self.n_head = n_head
self.d_head = d_head
self.d_inner = d_inner
self.hidden_act = hidden_act
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = 2
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.initializer_std = initializer_std
# Used in the tests to check the size of the first attention layer
self.num_attention_heads = n_head
# Used in the tests to check the size of the first hidden state
self.hidden_size = self.d_model
# Used in the tests to check the number of output hidden states/attentions
self.num_hidden_layers = sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
self.expected_num_hidden_layers = self.num_hidden_layers + 2
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
)
def get_config(self):
return FunnelConfig(
vocab_size=self.vocab_size,
block_sizes=self.block_sizes,
num_decoder_layers=self.num_decoder_layers,
d_model=self.d_model,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_inner,
hidden_act=self.hidden_act,
hidden_dropout=self.hidden_dropout,
attention_dropout=self.attention_dropout,
activation_dropout=self.activation_dropout,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_std=self.initializer_std,
)
def create_and_check_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model))
def create_and_check_base_model(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelBaseModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
model.config.truncate_seq = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model))
model.config.separate_cls = False
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model))
def create_and_check_for_pretraining(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=fake_token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_masked_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_choices = self.num_choices
model = FunnelForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_token_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = FunnelForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = FunnelForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FunnelModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(
FunnelModel,
FunnelForMaskedLM,
FunnelForPreTraining,
FunnelForQuestionAnswering,
FunnelForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": (FunnelBaseModel, FunnelModel),
"fill-mask": FunnelForMaskedLM,
"question-answering": FunnelForQuestionAnswering,
"text-classification": FunnelForSequenceClassification,
"token-classification": FunnelForTokenClassification,
"zero-shot": FunnelForSequenceClassification,
}
if is_torch_available()
else {}
)
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = FunnelModelTester(self)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
class FunnelBaseModelTest(ModelTesterMixin, unittest.TestCase):
test_head_masking = False
test_pruning = False
all_model_classes = (
(FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
)
def setUp(self):
self.model_tester = FunnelModelTester(self, base=True)
self.config_tester = ConfigTester(self, config_class=FunnelConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_base_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
# overwrite from test_modeling_common
def test_training(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class.__name__ == "FunnelBaseModel":
continue
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
if hasattr(module, param) and getattr(module, param) is not None:
weight = getattr(module, param)
weight.data.fill_(3)
@require_torch
@require_sentencepiece
@require_tokenizers
class FunnelModelIntegrationTest(unittest.TestCase):
def test_inference_tiny_model(self):
batch_size = 13
sequence_length = 7
input_ids = torch.arange(0, batch_size * sequence_length).long().reshape(batch_size, sequence_length)
lengths = [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
token_type_ids = torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths])
model = FunnelModel.from_pretrained("sgugger/funnel-random-tiny")
output = model(input_ids, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2344.8352)
expected_output_mean = torch.tensor(0.8052)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
attention_mask = torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]])
output = model(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)[0].abs()
expected_output_sum = torch.tensor(2343.8425)
expected_output_mean = torch.tensor(0.8049)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_model(self):
tokenizer = FunnelTokenizer.from_pretrained("huggingface/funnel-small")
model = FunnelModel.from_pretrained("huggingface/funnel-small")
inputs = tokenizer("Hello! I am the Funnel Transformer model.", return_tensors="pt")
output = model(**inputs)[0]
expected_output_sum = torch.tensor(235.7246)
expected_output_mean = torch.tensor(0.0256)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
| 19,994 | 37.013308 | 119 | py |
transformers | transformers-main/tests/models/efficientnet/test_image_processing_efficientnet.py | # coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import EfficientNetImageProcessor
class EfficientNetImageProcessorTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
):
size = size if size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
def prepare_image_processor_dict(self):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class EfficientNetImageProcessorTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = EfficientNetImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = EfficientNetImageProcessorTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
),
)
| 7,220 | 35.841837 | 111 | py |
transformers | transformers-main/tests/models/efficientnet/test_modeling_efficientnet.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch EfficientNet model. """
import inspect
import unittest
from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EfficientNetForImageClassification, EfficientNetModel
from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class EfficientNetModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
num_channels=3,
kernel_sizes=[3, 3, 5],
in_channels=[32, 16, 24],
out_channels=[16, 24, 40],
strides=[1, 1, 2],
num_block_repeats=[1, 1, 2],
expand_ratios=[1, 6, 6],
is_training=True,
use_labels=True,
intermediate_size=37,
hidden_act="gelu",
num_labels=10,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.kernel_sizes = kernel_sizes
self.in_channels = in_channels
self.out_channels = out_channels
self.strides = strides
self.num_block_repeats = num_block_repeats
self.expand_ratios = expand_ratios
self.is_training = is_training
self.hidden_act = hidden_act
self.num_labels = num_labels
self.use_labels = use_labels
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return EfficientNetConfig(
num_channels=self.num_channels,
kernel_sizes=self.kernel_sizes,
in_channels=self.in_channels,
out_channels=self.out_channels,
strides=self.strides,
num_block_repeats=self.num_block_repeats,
expand_ratios=self.expand_ratios,
hidden_act=self.hidden_act,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = EfficientNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# expected last hidden states: B, C, H // 4, W // 4
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = EfficientNetForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as EfficientNet does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = EfficientNetModelTester(self)
self.config_tester = ConfigTester(
self, config_class=EfficientNetConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
@unittest.skip(reason="EfficientNet does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="EfficientNet does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="EfficientNet does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
num_blocks = sum(config.num_block_repeats) * 4
self.assertEqual(len(hidden_states), num_blocks)
# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 2, self.model_tester.image_size // 2],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = EfficientNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@is_pipeline_test
@require_vision
@slow
def test_pipeline_image_classification(self):
super().test_pipeline_image_classification()
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class EfficientNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("google/efficientnet-b7") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([0.0001, 0.0002, 0.0002]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
| 10,285 | 36.540146 | 127 | py |
transformers | transformers-main/tests/models/camembert/test_modeling_tf_camembert.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class TFCamembertModelIntegrationTest(unittest.TestCase):
@slow
def test_output_embeds_base_model(self):
model = TFCamembertModel.from_pretrained("jplu/tf-camembert-base")
input_ids = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]],
dtype=tf.int32,
) # J'aime le camembert !"
output = model(input_ids)["last_hidden_state"]
expected_shape = tf.TensorShape((1, 10, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]],
dtype=tf.float32,
)
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
| 2,032 | 34.666667 | 98 | py |
transformers | transformers-main/tests/models/camembert/test_tokenization_camembert.py | # coding=utf-8
# Copyright 2018 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
SAMPLE_BPE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
FRAMEWORK = "pt" if is_torch_available() else "tf"
@require_sentencepiece
@require_tokenizers
class CamembertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = CamembertTokenizer
rust_tokenizer_class = CamembertTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = CamembertTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<pad>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<s>NOTUSED")
self.assertEqual(vocab_keys[1], "<pad>")
self.assertEqual(vocab_keys[-1], "<mask>")
self.assertEqual(len(vocab_keys), 1_004)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_005)
def test_rust_and_python_bpe_tokenizers(self):
tokenizer = CamembertTokenizer(SAMPLE_BPE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
rust_tokenizer = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
sequence = "I was born in 92000, and this is falsé."
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
tokens = tokenizer.convert_ids_to_tokens(ids)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
sequences = [
"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
"telles que la traduction et la synthèse de texte.",
]
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="camembert-base",
revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf",
sequences=sequences,
)
| 5,797 | 43.6 | 826 | py |
transformers | transformers-main/tests/models/camembert/test_modeling_camembert.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class CamembertModelIntegrationTest(unittest.TestCase):
@slow
def test_output_embeds_base_model(self):
model = CamembertModel.from_pretrained("camembert-base")
model.to(torch_device)
input_ids = torch.tensor(
[[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]],
device=torch_device,
dtype=torch.long,
) # J'aime le camembert !
with torch.no_grad():
output = model(input_ids)["last_hidden_state"]
expected_shape = torch.Size((1, 10, 768))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]],
device=torch_device,
dtype=torch.float,
)
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| 2,073 | 35.385965 | 115 | py |
transformers | transformers-main/tests/models/table_transformer/test_modeling_table_transformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Table Transformer model. """
import inspect
import math
import unittest
from huggingface_hub import hf_hub_download
from transformers import TableTransformerConfig, is_timm_available, is_vision_available
from transformers.testing_utils import require_timm, require_vision, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_timm_available():
import torch
from transformers import ResNetConfig, TableTransformerForObjectDetection, TableTransformerModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class TableTransformerModelTester:
def __init__(
self,
parent,
batch_size=8,
is_training=True,
use_labels=True,
hidden_size=256,
num_hidden_layers=2,
num_attention_heads=8,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
num_queries=12,
num_channels=3,
min_size=200,
max_size=200,
n_targets=8,
num_labels=91,
):
self.parent = parent
self.batch_size = batch_size
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.num_queries = num_queries
self.num_channels = num_channels
self.min_size = min_size
self.max_size = max_size
self.n_targets = n_targets
self.num_labels = num_labels
# we also set the expected seq length for both encoder and decoder
self.encoder_seq_length = math.ceil(self.min_size / 32) * math.ceil(self.max_size / 32)
self.decoder_seq_length = self.num_queries
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size])
pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_size], device=torch_device)
labels = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
labels = []
for i in range(self.batch_size):
target = {}
target["class_labels"] = torch.randint(
high=self.num_labels, size=(self.n_targets,), device=torch_device
)
target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device)
target["masks"] = torch.rand(self.n_targets, self.min_size, self.max_size, device=torch_device)
labels.append(target)
config = self.get_config()
return config, pixel_values, pixel_mask, labels
def get_config(self):
return TableTransformerConfig(
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
num_queries=self.num_queries,
num_labels=self.num_labels,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def create_and_check_table_transformer_model(self, config, pixel_values, pixel_mask, labels):
model = TableTransformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.decoder_seq_length, self.hidden_size)
)
def create_and_check_table_transformer_object_detection_head_model(self, config, pixel_values, pixel_mask, labels):
model = TableTransformerForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
def create_and_check_table_transformer_no_timm_backbone(self, config, pixel_values, pixel_mask, labels):
config.use_timm_backbone = False
config.backbone_config = ResNetConfig()
model = TableTransformerForObjectDetection(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1))
self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4))
@require_timm
class TableTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
TableTransformerModel,
TableTransformerForObjectDetection,
)
if is_timm_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
if is_timm_available()
else {}
)
is_encoder_decoder = True
test_torchscript = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
# special case for head models
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ in ["TableTransformerForObjectDetection"]:
labels = []
for i in range(self.model_tester.batch_size):
target = {}
target["class_labels"] = torch.ones(
size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long
)
target["boxes"] = torch.ones(
self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float
)
target["masks"] = torch.ones(
self.model_tester.n_targets,
self.model_tester.min_size,
self.model_tester.max_size,
device=torch_device,
dtype=torch.float,
)
labels.append(target)
inputs_dict["labels"] = labels
return inputs_dict
def setUp(self):
self.model_tester = TableTransformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=TableTransformerConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
def test_table_transformer_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_model(*config_and_inputs)
def test_table_transformer_object_detection_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_object_detection_head_model(*config_and_inputs)
def test_table_transformer_no_timm_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_table_transformer_no_timm_backbone(*config_and_inputs)
@unittest.skip(reason="Table Transformer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Table Transformer does not have a get_input_embeddings method")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="Table Transformer is not a generative model")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="Table Transformer does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@slow
def test_model_outputs_equivalence(self):
# TODO Niels: fix me!
pass
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
decoder_seq_length = self.model_tester.decoder_seq_length
encoder_seq_length = self.model_tester.encoder_seq_length
decoder_key_length = self.model_tester.decoder_seq_length
encoder_key_length = self.model_tester.encoder_seq_length
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
if self.is_encoder_decoder:
correct_outlen = 5
# loss is at first position
if "labels" in inputs_dict:
correct_outlen += 1 # loss is added to beginning
# Object Detection model returns pred_logits and pred_boxes
if model_class.__name__ == "TableTransformerForObjectDetection":
correct_outlen += 2
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[
self.model_tester.num_attention_heads,
decoder_seq_length,
encoder_key_length,
],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_retain_grad_hidden_states_attentions(self):
# removed retain_grad and grad on decoder_hidden_states, as queries don't require grad
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_attentions = outputs.encoder_attentions[0]
encoder_hidden_states.retain_grad()
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
if model.config.is_encoder_decoder:
expected_arg_names = ["pixel_values", "pixel_mask"]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" in arg_names
else []
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
else:
expected_arg_names = ["pixel_values", "pixel_mask"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_different_timm_backbone(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# let's pick a random timm backbone
config.backbone = "tf_mobilenetv3_small_075"
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if model_class.__name__ == "TableTransformerForObjectDetection":
expected_shape = (
self.model_tester.batch_size,
self.model_tester.num_queries,
self.model_tester.num_labels + 1,
)
self.assertEqual(outputs.logits.shape, expected_shape)
self.assertTrue(outputs)
def test_greyscale_images(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# use greyscale pixel values
inputs_dict["pixel_values"] = floats_tensor(
[self.model_tester.batch_size, 1, self.model_tester.min_size, self.model_tester.max_size]
)
# let's set num_channels to 1
config.num_channels = 1
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertTrue(outputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
configs_no_init.init_xavier_std = 1e9
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if "bbox_attention" in name and "bias" not in name:
self.assertLess(
100000,
abs(param.data.max().item()),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
TOLERANCE = 1e-4
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_timm
@require_vision
@slow
class TableTransformerModelIntegrationTests(unittest.TestCase):
def test_table_detection(self):
image_processor = AutoImageProcessor.from_pretrained("microsoft/table-transformer-detection")
model = TableTransformerForObjectDetection.from_pretrained("microsoft/table-transformer-detection")
model.to(torch_device)
file_path = hf_hub_download(repo_id="nielsr/example-pdf", repo_type="dataset", filename="example_pdf.png")
image = Image.open(file_path).convert("RGB")
inputs = image_processor(image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
expected_shape = (1, 15, 3)
self.assertEqual(outputs.logits.shape, expected_shape)
expected_logits = torch.tensor(
[[-6.7329, -16.9590, 6.7447], [-8.0038, -22.3071, 6.9288], [-7.2445, -20.9855, 7.3465]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4))
expected_boxes = torch.tensor(
[[0.4868, 0.1764, 0.6729], [0.6674, 0.4621, 0.3864], [0.4720, 0.1757, 0.6362]], device=torch_device
)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-3))
| 22,024 | 40.322702 | 119 | py |
transformers | transformers-main/tests/models/musicgen/test_modeling_musicgen.py | # coding=utf-8
# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Musicgen model. """
import copy
import inspect
import math
import unittest
import numpy as np
from transformers import (
EncodecConfig,
MusicgenConfig,
MusicgenDecoderConfig,
MusicgenProcessor,
PretrainedConfig,
T5Config,
)
from transformers.testing_utils import is_torch_available, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MusicgenForCausalLM,
MusicgenForConditionalGeneration,
MusicgenModel,
set_seed,
)
from transformers.generation import (
GreedySearchDecoderOnlyOutput,
GreedySearchEncoderDecoderOutput,
InfNanRemoveLogitsProcessor,
LogitsProcessorList,
SampleDecoderOnlyOutput,
SampleEncoderDecoderOutput,
)
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(configs_no_init, key, 1e-10)
if isinstance(getattr(configs_no_init, key, None), PretrainedConfig):
no_init_subconfig = _config_zero_init(getattr(configs_no_init, key))
setattr(configs_no_init, key, no_init_subconfig)
return configs_no_init
def prepare_musicgen_decoder_inputs_dict(
config,
input_ids,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])[:, 0, :]
attention_mask = attention_mask.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.num_hidden_layers, config.num_attention_heads, device=torch_device)
if encoder_attention_mask is None and encoder_hidden_states is not None:
encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.num_hidden_layers, config.num_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
"head_mask": head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class MusicgenDecoderTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=7,
is_training=False,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=100,
pad_token_id=99,
bos_token_id=99,
num_codebooks=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.num_codebooks = num_codebooks
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
config = self.get_config()
inputs_dict = prepare_musicgen_decoder_inputs_dict(
config, input_ids, encoder_hidden_states=encoder_hidden_states
)
return config, inputs_dict
def get_config(self):
config = MusicgenDecoderConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
d_ff=self.intermediate_size,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.bos_token_id,
bos_token_id=self.bos_token_id,
num_codebooks=self.num_codebooks,
tie_word_embeddings=False,
)
return config
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
@require_torch
class MusicgenDecoderTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MusicgenModel, MusicgenForCausalLM) if is_torch_available() else ()
greedy_sample_model_classes = (
(MusicgenForCausalLM,) if is_torch_available() else ()
) # we don't want to run all the generation tests, only a specific subset
pipeline_model_mapping = {}
test_pruning = False
test_resize_embeddings = False
def setUp(self):
self.model_tester = MusicgenDecoderTester(self)
self.config_tester = ConfigTester(self, config_class=MusicgenDecoderConfig, hidden_size=16)
def test_config(self):
self.config_tester.run_common_tests()
# override since we have to compute the input embeddings over codebooks
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_ids = inputs["input_ids"]
del inputs["input_ids"]
embed_tokens = model.get_input_embeddings()
input_ids = input_ids.reshape(-1, config.num_codebooks, input_ids.shape[-1])
inputs["inputs_embeds"] = sum(
[embed_tokens[codebook](input_ids[:, codebook]) for codebook in range(config.num_codebooks)]
)
with torch.no_grad():
model(**inputs)[0]
# override since we have embeddings / LM heads over multiple codebooks
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
first_embed = model.get_input_embeddings()[0]
self.assertIsInstance(first_embed, torch.nn.Embedding)
lm_heads = model.get_output_embeddings()
self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))
# skip as this model doesn't support all arguments tested
def test_model_outputs_equivalence(self):
pass
# skip as this model has multiple inputs embeds and lm heads that should not be tied
def test_tie_model_weights(self):
pass
# skip as this model has multiple inputs embeds and lm heads that should not be tied
def test_tied_weights_keys(self):
pass
def _get_input_ids_and_config(self, batch_size=2):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
# take max batch_size
sequence_length = input_ids.shape[-1]
input_ids = input_ids[: batch_size * config.num_codebooks, :]
# generate max 3 tokens
max_length = input_ids.shape[-1] + 3
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long)
return config, input_ids, attention_mask, max_length
@staticmethod
def _get_logits_processor_and_kwargs(
input_length,
eos_token_id,
forced_bos_token_id=None,
forced_eos_token_id=None,
max_length=None,
diversity_penalty=None,
):
process_kwargs = {
"min_length": input_length + 1 if max_length is None else max_length - 1,
}
logits_processor = LogitsProcessorList()
return process_kwargs, logits_processor
# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
# additional post-processing in the former
def test_greedy_generate_dict_outputs(self):
for model_class in self.greedy_sample_model_classes:
# disable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput)
self.assertNotIn(config.pad_token_id, output_generate)
# override since we don't expect the outputs of `.generate` and `.greedy_search` to be the same, since we perform
# additional post-processing in the former
def test_greedy_generate_dict_outputs_use_cache(self):
for model_class in self.greedy_sample_model_classes:
# enable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_greedy, GreedySearchDecoderOnlyOutput)
self.assertIsInstance(output_generate, GreedySearchDecoderOnlyOutput)
# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
# additional post-processing in the former
def test_sample_generate(self):
for model_class in self.greedy_sample_model_classes:
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
# check `generate()` and `sample()` are equal
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=3,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
)
self.assertIsInstance(output_sample, torch.Tensor)
self.assertIsInstance(output_generate, torch.Tensor)
# override since we don't expect the outputs of `.generate` and `.sample` to be the same, since we perform
# additional post-processing in the former
def test_sample_generate_dict_output(self):
for model_class in self.greedy_sample_model_classes:
# disable cache
config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
max_length=max_length,
num_return_sequences=1,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_sample, SampleDecoderOnlyOutput)
self.assertIsInstance(output_generate, SampleDecoderOnlyOutput)
def prepare_musicgen_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.reshape(
-1, config.decoder.num_codebooks, decoder_input_ids.shape[-1]
)[:, 0, :]
decoder_attention_mask = decoder_attention_mask.ne(config.decoder.pad_token_id)
if head_mask is None:
head_mask = torch.ones(
config.text_encoder.num_hidden_layers, config.text_encoder.num_attention_heads, device=torch_device
)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(
config.decoder.num_hidden_layers, config.decoder.num_attention_heads, device=torch_device
)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(
config.decoder.num_hidden_layers, config.decoder.num_attention_heads, device=torch_device
)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class MusicgenTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=7,
is_training=False,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=100,
pad_token_id=99,
bos_token_id=99,
num_codebooks=4,
num_filters=4,
codebook_size=128,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.num_codebooks = num_codebooks
self.num_filters = num_filters
self.codebook_size = codebook_size
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
decoder_input_ids = ids_tensor([self.batch_size * self.num_codebooks, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_musicgen_inputs_dict(config, input_ids, decoder_input_ids=decoder_input_ids)
return config, inputs_dict
def get_config(self):
text_encoder_config = T5Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
d_ff=self.intermediate_size,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
)
audio_encoder_config = EncodecConfig(
hidden_size=self.vocab_size,
compress=1,
num_filters=self.num_filters,
codebook_size=self.codebook_size,
codebook_dim=self.vocab_size,
)
decoder_config = MusicgenDecoderConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
ffn_dim=self.intermediate_size,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.bos_token_id,
bos_token_id=self.bos_token_id,
num_codebooks=self.num_codebooks,
tie_word_embeddings=False,
)
config = MusicgenConfig.from_sub_models_config(text_encoder_config, audio_encoder_config, decoder_config)
return config
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
@require_torch
class MusicgenTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else ()
greedy_sample_model_classes = (MusicgenForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = {}
test_pruning = False # training is not supported yet for MusicGen
test_headmasking = False
test_resize_embeddings = False
# not to test torchscript as the model tester doesn't prepare `input_values` and `padding_mask`
# (and `torchscript` hates `None` values).
test_torchscript = False
def setUp(self):
self.model_tester = MusicgenTester(self)
def _check_output_with_attentions(self, outputs, config, input_ids, decoder_input_ids):
text_encoder_config = config.text_encoder
decoder_config = config.decoder
encoder_attentions = outputs["encoder_attentions"]
self.assertEqual(len(encoder_attentions), text_encoder_config.num_hidden_layers)
self.assertEqual(
encoder_attentions[0].shape[-3:],
(text_encoder_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),
)
decoder_attentions = outputs["decoder_attentions"]
num_decoder_layers = decoder_config.num_hidden_layers
self.assertEqual(len(decoder_attentions), num_decoder_layers)
self.assertEqual(
decoder_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
)
cross_attentions = outputs["cross_attentions"]
self.assertEqual(len(cross_attentions), num_decoder_layers)
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
self.assertEqual(
cross_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, cross_attention_input_seq_len, input_ids.shape[-1]),
)
def check_musicgen_model_output_attentions(
self,
model_class,
config,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_attentions=True,
**kwargs,
)
self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)
def check_musicgen_model_output_attentions_from_config(
self,
model_class,
config,
input_ids,
attention_mask,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
# Similar to `check_musicgen_model_output_attentions`, but with `output_attentions` triggered from the
# config file. Contrarily to most models, changing the model's config won't work -- the defaults are loaded
# from the inner models' configurations.
config.output_attentions = True # model config -> won't work
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
**kwargs,
)
self.assertTrue(
all(key not in outputs for key in ["encoder_attentions", "decoder_attentions", "cross_attentions"])
)
config.text_encoder.output_attentions = True # inner model config -> will work
config.audio_encoder.output_attentions = True
config.decoder.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
**kwargs,
)
self._check_output_with_attentions(outputs, config, input_ids, decoder_input_ids)
# override since changing `output_attentions` from the top-level model config won't work
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.check_musicgen_model_output_attentions(model_class, config, **inputs_dict)
self.check_musicgen_model_output_attentions_from_config(model_class, config, **inputs_dict)
# override since we have a specific forward signature for musicgen
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"input_ids",
"attention_mask",
"input_values",
"padding_mask",
"decoder_input_ids",
"decoder_attention_mask",
]
expected_arg_names.extend(
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
else ["encoder_outputs"]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
# override since changing `gradient_checkpointing` from the top-level model config won't work
def test_gradient_checkpointing_backward_compatibility(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
if not model_class.supports_gradient_checkpointing:
continue
config.text_encoder.gradient_checkpointing = True
config.audio_encoder.gradient_checkpointing = True
config.decoder.gradient_checkpointing = True
model = model_class(config)
self.assertTrue(model.is_gradient_checkpointing)
# skip as this model has multiple inputs embeds and lm heads that should not be tied
def test_tie_model_weights(self):
pass
# skip as this model has multiple inputs embeds and lm heads that should not be tied
def test_tied_model_weights_key_ignore(self):
pass
# skip as this model has multiple inputs embeds and lm heads that should not be tied
def test_tied_weights_keys(self):
pass
# override since changing `output_hidden_states` / `output_attentions` from the top-level model config won't work
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.text_encoder.output_hidden_states = True
config.audio_encoder.output_hidden_states = True
config.decoder.output_hidden_states = True
config.text_encoder.output_attentions = True
config.decoder.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
decoder_hidden_states = outputs.decoder_hidden_states[0]
decoder_hidden_states.retain_grad()
if self.has_attentions:
encoder_attentions = outputs.encoder_attentions[0]
encoder_attentions.retain_grad()
decoder_attentions = outputs.decoder_attentions[0]
decoder_attentions.retain_grad()
cross_attentions = outputs.cross_attentions[0]
cross_attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(decoder_hidden_states.grad)
if self.has_attentions:
self.assertIsNotNone(encoder_attentions.grad)
self.assertIsNotNone(decoder_attentions.grad)
self.assertIsNotNone(cross_attentions.grad)
# override since changing `output_hidden_states` from the top-level model config won't work
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states
expected_num_layers = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
hidden_states = outputs.decoder_hidden_states
self.assertIsInstance(hidden_states, (list, tuple))
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.text_encoder.output_hidden_states = True
config.audio_encoder.output_hidden_states = True
config.decoder.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
# override since the conv layers and lstm's in encodec are exceptions
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = ["conv"]
ignore_init = ["lstm"]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
elif not any(x in name for x in ignore_init):
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# override since we have embeddings / LM heads over multiple codebooks
def test_model_common_attributes(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), torch.nn.Embedding)
lm_heads = model.get_output_embeddings()
self.assertTrue(lm_heads is None or isinstance(lm_heads[0], torch.nn.Linear))
def _get_input_ids_and_config(self, batch_size=2):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = inputs_dict["input_ids"]
# take max batch_size
sequence_length = input_ids.shape[-1]
input_ids = input_ids[:batch_size, :]
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long)
# generate max 3 tokens
decoder_input_ids = inputs_dict["decoder_input_ids"]
max_length = decoder_input_ids.shape[-1] + 3
decoder_input_ids = decoder_input_ids[: batch_size * config.decoder.num_codebooks, :]
return config, input_ids, attention_mask, decoder_input_ids, max_length
# override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen (input / outputs are
# different modalities -> different shapes)
def _greedy_generate(
self,
model,
input_ids,
attention_mask,
decoder_input_ids,
max_length,
output_scores=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
logits_process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
eos_token_id=model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=False,
num_beams=1,
max_length=max_length,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
remove_invalid_values=True,
**logits_process_kwargs,
**model_kwargs,
)
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_greedy = model.greedy_search(
decoder_input_ids,
max_length=max_length,
logits_processor=logits_processor,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_scores=output_scores,
return_dict_in_generate=return_dict_in_generate,
encoder_outputs=encoder_outputs,
**model_kwargs,
)
return output_greedy, output_generate
# override since the `input_ids` cannot be used as the `decoder_input_ids` for musicgen (input / outputs are
# different modalities -> different shapes)
def _sample_generate(
self,
model,
input_ids,
attention_mask,
decoder_input_ids,
max_length,
num_return_sequences,
logits_processor,
logits_warper,
logits_warper_kwargs,
process_kwargs,
output_scores=False,
output_attentions=False,
output_hidden_states=False,
return_dict_in_generate=False,
):
torch.manual_seed(0)
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_generate = model.generate(
input_ids,
do_sample=True,
num_beams=1,
max_length=max_length,
num_return_sequences=num_return_sequences,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
remove_invalid_values=True,
**logits_warper_kwargs,
**process_kwargs,
**model_kwargs,
)
torch.manual_seed(0)
encoder_outputs, input_ids, attention_mask = self._get_encoder_outputs(
model,
input_ids,
attention_mask,
num_interleave=num_return_sequences,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
# prevent flaky generation test failures
logits_processor.append(InfNanRemoveLogitsProcessor())
with torch.no_grad():
model_kwargs = {"attention_mask": attention_mask} if attention_mask is not None else {}
output_sample = model.sample(
decoder_input_ids.repeat_interleave(num_return_sequences, dim=0),
max_length=max_length,
logits_processor=logits_processor,
logits_warper=logits_warper,
output_scores=output_scores,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict_in_generate=return_dict_in_generate,
encoder_outputs=encoder_outputs,
**model_kwargs,
)
return output_sample, output_generate
@staticmethod
def _get_logits_processor_and_kwargs(
input_length,
eos_token_id,
forced_bos_token_id=None,
forced_eos_token_id=None,
max_length=None,
diversity_penalty=None,
):
process_kwargs = {
"min_length": input_length + 1 if max_length is None else max_length - 1,
}
logits_processor = LogitsProcessorList()
return process_kwargs, logits_processor
def test_greedy_generate_dict_outputs(self):
for model_class in self.greedy_sample_model_classes:
# disable cache
config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
max_length=max_length,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput)
self.assertNotIn(config.pad_token_id, output_generate)
def test_greedy_generate_dict_outputs_use_cache(self):
for model_class in self.greedy_sample_model_classes:
# enable cache
config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
config.use_cache = True
config.is_decoder = True
model = model_class(config).to(torch_device).eval()
output_greedy, output_generate = self._greedy_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
max_length=max_length,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_greedy, GreedySearchEncoderDecoderOutput)
self.assertIsInstance(output_generate, GreedySearchEncoderDecoderOutput)
def test_sample_generate(self):
for model_class in self.greedy_sample_model_classes:
config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
model = model_class(config).to(torch_device).eval()
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=2)
# check `generate()` and `sample()` are equal
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
max_length=max_length,
num_return_sequences=1,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
)
self.assertIsInstance(output_sample, torch.Tensor)
self.assertIsInstance(output_generate, torch.Tensor)
def test_sample_generate_dict_output(self):
for model_class in self.greedy_sample_model_classes:
# disable cache
config, input_ids, attention_mask, decoder_input_ids, max_length = self._get_input_ids_and_config()
config.use_cache = False
model = model_class(config).to(torch_device).eval()
process_kwargs, logits_processor = self._get_logits_processor_and_kwargs(
input_ids.shape[-1],
model.config.eos_token_id,
forced_bos_token_id=model.config.forced_bos_token_id,
forced_eos_token_id=model.config.forced_eos_token_id,
max_length=max_length,
)
logits_warper_kwargs, logits_warper = self._get_warper_and_kwargs(num_beams=1)
output_sample, output_generate = self._sample_generate(
model=model,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
max_length=max_length,
num_return_sequences=3,
logits_processor=logits_processor,
logits_warper=logits_warper,
logits_warper_kwargs=logits_warper_kwargs,
process_kwargs=process_kwargs,
output_scores=True,
output_hidden_states=True,
output_attentions=True,
return_dict_in_generate=True,
)
self.assertIsInstance(output_sample, SampleEncoderDecoderOutput)
self.assertIsInstance(output_generate, SampleEncoderDecoderOutput)
def test_generate_without_input_ids(self):
config, _, _, _, max_length = self._get_input_ids_and_config()
# if no bos token id => cannot generate from None
if config.bos_token_id is None:
return
for model_class in self.greedy_sample_model_classes:
model = model_class(config).to(torch_device)
model.eval()
output_ids_generate = model.generate(do_sample=False, max_length=max_length, remove_invalid_values=True)
self.assertIsNotNone(output_ids_generate)
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.greedy_sample_model_classes:
model = model_class(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(**input_dict, max_new_tokens=10)
model.generate(**input_dict, do_sample=True, max_new_tokens=10)
def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
"""Produces a series of 'bip bip' sounds at a given frequency."""
timesteps = np.arange(int(duration * sample_rate)) / sample_rate
wav = np.cos(2 * math.pi * 440 * timesteps)
time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
envelope = time_period >= 0.5
return wav * envelope
def place_dict_on_device(dict_to_place, device):
for key in dict_to_place:
if dict_to_place[key] is not None and isinstance(dict_to_place[key], torch.Tensor):
dict_to_place[key] = dict_to_place[key].to(device)
return dict_to_place
@require_torch
class MusicgenIntegrationTests(unittest.TestCase):
@cached_property
def model(self):
return MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small").to(torch_device)
@cached_property
def processor(self):
return MusicgenProcessor.from_pretrained("facebook/musicgen-small")
@slow
def test_logits_text_prompt(self):
model = self.model
processor = self.processor
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
# prepare the encoder inputs
input_ids = inputs.input_ids.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
# prepare the decoder inputs
pad_token_id = model.generation_config.pad_token_id
decoder_input_ids = (
torch.ones((input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long).to(torch_device)
* pad_token_id
)
with torch.no_grad():
logits = model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
).logits
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
-0.9708, -3.0149, -4.6415, -1.4754, -0.2786, -2.3523, -2.6049, -6.7467,
-1.0206, -3.2984, -3.3968, -1.5108, -1.5786, -3.1493, -1.1503, -0.0545,
]
)
# fmt: on
self.assertTrue(logits.shape == (*decoder_input_ids.shape, model.decoder.config.vocab_size))
self.assertTrue(torch.allclose(logits[0, 0, :16].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_logits_text_audio_prompt(self):
model = self.model
processor = self.processor
audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
text = ["80s music", "Club techno"]
inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")
# prepare the text encoder inputs
input_ids = inputs.input_ids.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
# prepare the audio encoder inputs
input_values = inputs.input_values.to(torch_device)
padding_mask = inputs.padding_mask.to(torch_device)
with torch.no_grad():
logits = model(
input_ids,
attention_mask=attention_mask,
input_values=input_values,
padding_mask=padding_mask,
).logits
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
0.1841, -2.9324, -0.7898, 0.1857, 0.4971, -2.8685, -1.6525, -1.6541,
2.7757, -2.5942, -3.0959, -1.0120, -1.0147, -0.4605, -0.8885, 0.6820,
]
)
# fmt: on
self.assertTrue(logits.shape == (8, 50, 2048))
self.assertTrue(torch.allclose(logits[0, -1, :16].cpu(), EXPECTED_LOGITS, atol=1e-4))
@slow
def test_generate_unconditional_greedy(self):
model = self.model
# only generate 1 sample with greedy - since it's deterministic all elements of the batch will be the same
unconditional_inputs = model.get_unconditional_inputs(num_samples=1)
unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device)
output_values = model.generate(**unconditional_inputs, do_sample=False, max_new_tokens=5)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
0.0056, 0.0064, 0.0063, 0.0054, 0.0042, 0.0033, 0.0024, 0.0015,
0.0015, 0.0010, 0.0004, -0.0012, -0.0036, -0.0055, -0.0067, -0.0071,
]
)
# fmt: on
self.assertTrue(output_values.shape == (1, 1, 3200))
self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4))
@slow
def test_generate_unconditional_sampling(self):
model = self.model
# for stochastic sampling we can generate multiple outputs
unconditional_inputs = model.get_unconditional_inputs(num_samples=2)
unconditional_inputs = place_dict_on_device(unconditional_inputs, device=torch_device)
set_seed(0)
output_values = model.generate(**unconditional_inputs, do_sample=True, max_new_tokens=10)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
0.0765, 0.0758, 0.0749, 0.0759, 0.0759, 0.0771, 0.0775, 0.0760,
0.0762, 0.0765, 0.0767, 0.0760, 0.0738, 0.0714, 0.0713, 0.0730,
]
)
# fmt: on
self.assertTrue(output_values.shape == (2, 1, 4480))
self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4))
@slow
def test_generate_text_prompt_greedy(self):
model = self.model
processor = self.processor
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
# prepare the encoder inputs
input_ids = inputs.input_ids.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
output_values = model.generate(
input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=None, max_new_tokens=10
)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
-1.1998e-04, -2.2302e-04, 4.6296e-04, 1.0524e-03, 2.4827e-04,
-4.0288e-05, -1.2468e-04, 4.9846e-05, 7.1485e-04, 4.4197e-04,
]
)
# fmt: on
self.assertTrue(output_values.shape == (2, 1, 4480))
self.assertTrue(torch.allclose(output_values[0, 0, :10].cpu(), EXPECTED_VALUES, atol=1e-4))
@slow
def test_generate_text_prompt_greedy_with_classifier_free_guidance(self):
model = self.model
processor = self.processor
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
# prepare the encoder inputs
input_ids = inputs.input_ids.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
output_values = model.generate(
input_ids, attention_mask=attention_mask, do_sample=False, guidance_scale=3, max_new_tokens=10
)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
0.0283, 0.0246, 0.0650, 0.0640, 0.0599, 0.0711, 0.0420, 0.0112,
0.0511, 0.0746, 0.1363, 0.1213, 0.0185, -0.0578, -0.0908, 0.0443,
]
)
# fmt: on
self.assertTrue(output_values.shape == (2, 1, 4480))
self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4))
@slow
def test_generate_text_prompt_sampling(self):
model = self.model
processor = self.processor
inputs = processor(text=["80s music", "Club techno"], padding=True, return_tensors="pt")
# prepare the encoder inputs
input_ids = inputs.input_ids.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
set_seed(0)
output_values = model.generate(
input_ids, attention_mask=attention_mask, do_sample=True, guidance_scale=None, max_new_tokens=10
)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
-0.0047, -0.0094, -0.0028, -0.0018, -0.0057, -0.0007, -0.0104, -0.0211,
-0.0097, -0.0150, -0.0066, -0.0004, -0.0201, -0.0325, -0.0326, -0.0098,
]
)
# fmt: on
self.assertTrue(output_values.shape == (2, 1, 4480))
self.assertTrue(torch.allclose(output_values[0, 0, :16].cpu(), EXPECTED_VALUES, atol=1e-4))
@slow
def test_generate_text_audio_prompt(self):
model = self.model
processor = self.processor
audio = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
text = ["80s music", "Club techno"]
inputs = processor(audio=audio, text=text, padding=True, return_tensors="pt")
inputs = place_dict_on_device(inputs, device=torch_device)
output_values = model.generate(**inputs, do_sample=False, guidance_scale=None, max_new_tokens=10)
# fmt: off
EXPECTED_VALUES = torch.tensor(
[
-0.0036, -0.0130, -0.0261, -0.0384, -0.0557, -0.0718, -0.0680, -0.0632,
-0.0529, -0.0403, -0.0289, -0.0198, -0.0136, -0.0101, -0.0095, -0.0040,
]
)
# fmt: on
self.assertTrue(
output_values.shape == (2, 1, 36480)
) # input values take shape 32000 and we generate from there
self.assertTrue(torch.allclose(output_values[0, 0, -16:].cpu(), EXPECTED_VALUES, atol=1e-4))
| 53,847 | 38.887407 | 117 | py |
transformers | transformers-main/tests/models/musicgen/test_processing_musicgen.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the MusicGen processor."""
import random
import shutil
import tempfile
import unittest
import numpy as np
from transformers import T5Tokenizer, T5TokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available
if is_torch_available():
pass
if is_speech_available():
from transformers import EncodecFeatureExtractor, MusicgenProcessor
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
@require_torch
@require_sentencepiece
class MusicgenProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "facebook/musicgen-small"
self.tmpdirname = tempfile.mkdtemp()
def get_tokenizer(self, **kwargs):
return T5Tokenizer.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return EncodecFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = MusicgenProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, EncodecFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = MusicgenProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
processor = MusicgenProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, EncodecFeatureExtractor)
def test_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = floats_list((3, 1000))
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
input_processor = processor(raw_speech, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
input_str = "This is a test string"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(sequences=predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
feature_extractor.model_input_names,
msg="`processor` and `feature_extractor` model input names do not match",
)
def test_decode_audio(self):
feature_extractor = self.get_feature_extractor(padding_side="left")
tokenizer = self.get_tokenizer()
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
raw_speech = [floats_list((1, x))[0] for x in range(5, 20, 5)]
padding_mask = processor(raw_speech).padding_mask
generated_speech = np.asarray(floats_list((3, 20)))[:, None, :]
decoded_audios = processor.batch_decode(generated_speech, padding_mask=padding_mask)
self.assertIsInstance(decoded_audios, list)
for audio in decoded_audios:
self.assertIsInstance(audio, np.ndarray)
self.assertTrue(decoded_audios[0].shape == (1, 10))
self.assertTrue(decoded_audios[1].shape == (1, 15))
self.assertTrue(decoded_audios[2].shape == (1, 20))
| 6,510 | 36.41954 | 117 | py |
transformers | transformers-main/tests/models/distilbert/test_modeling_distilbert.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class DistilBertModelTester(object):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return DistilBertConfig(
vocab_size=self.vocab_size,
dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
hidden_dim=self.intermediate_size,
hidden_act=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
)
def create_and_check_distilbert_model(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DistilBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_distilbert_for_masked_lm(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DistilBertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_distilbert_for_question_answering(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = DistilBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_distilbert_for_sequence_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = DistilBertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_distilbert_for_token_classification(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = DistilBertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_distilbert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = DistilBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class DistilBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = True
test_resize_embeddings = True
test_resize_position_embeddings = True
def setUp(self):
self.model_tester = DistilBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_distilbert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DistilBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
@require_torch_gpu
def test_torchscript_device_change(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
config.torchscript = True
model = model_class(config=config)
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
traced_model = torch.jit.trace(
model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
)
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt"))
loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
@require_torch
class DistilBertModelIntergrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = DistilBertModel.from_pretrained("distilbert-base-uncased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
| 12,638 | 40.439344 | 117 | py |
transformers | transformers-main/tests/models/distilbert/test_modeling_flax_distilbert.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class FlaxDistilBertModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = DistilBertConfig(
vocab_size=self.vocab_size,
dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
hidden_dim=self.intermediate_size,
hidden_act=self.hidden_act,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
tie_weights_=True,
)
return config, input_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class FlaxDistilBertModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def setUp(self):
self.model_tester = FlaxDistilBertModelTester(self)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("distilbert-base-uncased")
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
@require_flax
class FlaxDistilBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = FlaxDistilBertModel.from_pretrained("distilbert-base-uncased")
input_ids = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = (1, 11, 768)
self.assertEqual(output.shape, expected_shape)
expected_slice = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
| 5,644 | 35.895425 | 118 | py |
transformers | transformers-main/tests/models/auto/test_modeling_tf_auto.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_tensorflow_probability,
require_tf,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
if is_tf_available():
from transformers import (
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeq2SeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelForTableQuestionAnswering,
TFAutoModelForTokenClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFFunnelBaseModel,
TFFunnelModel,
TFGPT2LMHeadModel,
TFRobertaForMaskedLM,
TFT5ForConditionalGeneration,
TFTapasForQuestionAnswering,
)
from transformers.models.auto.modeling_tf_auto import (
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_MAPPING,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class NewModelConfig(BertConfig):
model_type = "new-model"
if is_tf_available():
class TFNewModel(TFBertModel):
config_class = NewModelConfig
@require_tf
class TFAutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
model_name = "bert-base-cased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertModel)
@slow
def test_model_for_pretraining_from_pretrained(self):
model_name = "bert-base-cased"
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
@slow
def test_model_for_causal_lm(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = TFAutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_masked_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
@slow
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
@slow
@require_tensorflow_probability
def test_table_question_answering_model_from_pretrained(self):
for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFTapasForQuestionAnswering)
def test_from_pretrained_identifier(self):
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(model, TFBertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_identifier_from_model_type(self):
model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
self.assertIsInstance(model, TFRobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_pretrained_with_tuple_values(self):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny")
self.assertIsInstance(model, TFFunnelModel)
config = copy.deepcopy(model.config)
config.architectures = ["FunnelBaseModel"]
model = TFAutoModel.from_config(config)
self.assertIsInstance(model, TFFunnelBaseModel)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
model = TFAutoModel.from_pretrained(tmp_dir)
self.assertIsInstance(model, TFFunnelBaseModel)
def test_new_model_registration(self):
try:
AutoConfig.register("new-model", NewModelConfig)
auto_classes = [
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSequenceClassification,
TFAutoModelForTokenClassification,
]
for auto_class in auto_classes:
with self.subTest(auto_class.__name__):
# Wrong config class will raise an error
with self.assertRaises(ValueError):
auto_class.register(BertConfig, TFNewModel)
auto_class.register(NewModelConfig, TFNewModel)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(ValueError):
auto_class.register(BertConfig, TFBertModel)
# Now that the config is registered, it can be used as any other config with the auto-API
tiny_config = BertModelTester(self).get_config()
config = NewModelConfig(**tiny_config.to_dict())
model = auto_class.from_config(config)
self.assertIsInstance(model, TFNewModel)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = auto_class.from_pretrained(tmp_dir)
self.assertIsInstance(new_model, TFNewModel)
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
for mapping in (
TF_MODEL_MAPPING,
TF_MODEL_FOR_PRETRAINING_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_MASKED_LM_MAPPING,
):
if NewModelConfig in mapping._extra_content:
del mapping._extra_content[NewModelConfig]
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
):
_ = TFAutoModel.from_pretrained("bert-base")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_model_file_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin",
):
_ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model")
def test_model_from_pt_suggestion(self):
with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"):
_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
def test_cached_model_has_minimum_calls_to_head(self):
# Make sure we have cached the model.
_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with RequestCounter() as counter:
_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
self.assertEqual(counter.get_request_count, 0)
self.assertEqual(counter.head_request_count, 1)
self.assertEqual(counter.other_request_count, 0)
# With a sharded checkpoint
_ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
with RequestCounter() as counter:
_ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
self.assertEqual(counter.get_request_count, 0)
self.assertEqual(counter.head_request_count, 1)
self.assertEqual(counter.other_request_count, 0)
| 13,164 | 41.467742 | 115 | py |
transformers | transformers-main/tests/models/auto/test_modeling_flax_auto.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class FlaxAutoModelTest(unittest.TestCase):
@slow
def test_bert_from_pretrained(self):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(model_name):
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = FlaxAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, FlaxBertModel)
@slow
def test_roberta_from_pretrained(self):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(model_name):
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = FlaxAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, FlaxRobertaModel)
@slow
def test_bert_jax_jit(self):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = FlaxBertModel.from_pretrained(model_name)
tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
@jax.jit
def eval(**kwargs):
return model(**kwargs)
eval(**tokens).block_until_ready()
@slow
def test_roberta_jax_jit(self):
for model_name in ["roberta-base", "roberta-large"]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = FlaxRobertaModel.from_pretrained(model_name)
tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
@jax.jit
def eval(**kwargs):
return model(**kwargs)
eval(**tokens).block_until_ready()
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
):
_ = FlaxAutoModel.from_pretrained("bert-base")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = FlaxAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_model_file_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack",
):
_ = FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model")
def test_model_from_pt_suggestion(self):
with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"):
_ = FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
| 4,172 | 39.514563 | 108 | py |
transformers | transformers-main/tests/models/auto/test_modeling_tf_pytorch.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPT2Config,
T5Config,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeq2SeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPT2LMHeadModel,
TFRobertaForMaskedLM,
TFT5ForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPT2LMHeadModel,
RobertaForMaskedLM,
T5ForConditionalGeneration,
)
@is_pt_tf_cross_test
class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModel.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertModel)
model = AutoModel.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
@slow
def test_model_for_pretraining_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForPreTraining.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
model = AutoModelForPreTraining.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
@slow
def test_model_for_causal_lm(self):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = TFAutoModelForCausalLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
model = AutoModelForCausalLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelWithLMHead.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelWithLMHead.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_masked_lm(self):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForMaskedLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelForMaskedLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, from_pt=True)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForSequenceClassification.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
model = AutoModelForSequenceClassification.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
@slow
def test_question_answering_model_from_pretrained(self):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = TFAutoModelForQuestionAnswering.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
model = AutoModelForQuestionAnswering.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
def test_from_pretrained_identifier(self):
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_pt=True)
self.assertIsInstance(model, TFBertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_tf=True)
self.assertIsInstance(model, BertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_identifier_from_model_type(self):
model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, from_pt=True)
self.assertIsInstance(model, TFRobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, from_tf=True)
self.assertIsInstance(model, RobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
| 10,080 | 41.357143 | 114 | py |
transformers | transformers-main/tests/models/auto/test_modeling_auto.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import sys
import tempfile
import unittest
from collections import OrderedDict
from pathlib import Path
import pytest
import transformers
from transformers import BertConfig, GPT2Model, is_safetensors_available, is_torch_available
from transformers.models.auto.configuration_auto import CONFIG_MAPPING
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
require_torch,
slow,
)
from ..bert.test_modeling_bert import BertModelTester
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
if is_torch_available():
import torch
from test_module.custom_modeling import CustomModel
from transformers import (
AutoBackbone,
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelForTableQuestionAnswering,
AutoModelForTokenClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertModel,
FunnelBaseModel,
FunnelModel,
GPT2Config,
GPT2LMHeadModel,
ResNetBackbone,
RobertaForMaskedLM,
T5Config,
T5ForConditionalGeneration,
TapasConfig,
TapasForQuestionAnswering,
TimmBackbone,
)
from transformers.models.auto.modeling_auto import (
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
)
from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
@require_torch
class AutoModelTest(unittest.TestCase):
def setUp(self):
transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
self.assertEqual(len(loading_info["missing_keys"]), 0)
# When using PyTorch checkpoint, the expected value is `8`. With `safetensors` checkpoint (if it is
# installed), the expected value becomes `7`.
EXPECTED_NUM_OF_UNEXPECTED_KEYS = 7 if is_safetensors_available() else 8
self.assertEqual(len(loading_info["unexpected_keys"]), EXPECTED_NUM_OF_UNEXPECTED_KEYS)
self.assertEqual(len(loading_info["mismatched_keys"]), 0)
self.assertEqual(len(loading_info["error_msgs"]), 0)
@slow
def test_model_for_pretraining_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
# Only one value should not be initialized and in the missing keys.
for key, value in loading_info.items():
self.assertEqual(len(value), 0)
@slow
def test_lmhead_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelWithLMHead.from_pretrained(model_name)
model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_causal_lm(self):
for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, GPT2Config)
model = AutoModelForCausalLM.from_pretrained(model_name)
model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
@slow
def test_model_for_masked_lm(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_model_for_encoder_decoder_lm(self):
for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, T5Config)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
@slow
def test_sequence_classification_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model, loading_info = AutoModelForSequenceClassification.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
@slow
def test_question_answering_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
@slow
def test_table_question_answering_model_from_pretrained(self):
for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, TapasConfig)
model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TapasForQuestionAnswering)
@slow
def test_token_classification_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForTokenClassification.from_pretrained(model_name)
model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForTokenClassification)
@slow
def test_auto_backbone_timm_model_from_pretrained(self):
# Configs can't be loaded for timm models
model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True)
with pytest.raises(ValueError):
# We can't pass output_loading_info=True as we're loading from timm
AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TimmBackbone)
# Check kwargs are correctly passed to the backbone
model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_indices=(-1, -2))
self.assertEqual(model.out_indices, (-1, -2))
# Check out_features cannot be passed to Timm backbones
with self.assertRaises(ValueError):
_ = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_features=["stage1"])
@slow
def test_auto_backbone_from_pretrained(self):
model = AutoBackbone.from_pretrained("microsoft/resnet-18")
model, loading_info = AutoBackbone.from_pretrained("microsoft/resnet-18", output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, ResNetBackbone)
# Check kwargs are correctly passed to the backbone
model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_indices=[-1, -2])
self.assertEqual(model.out_indices, [-1, -2])
self.assertEqual(model.out_features, ["stage4", "stage3"])
model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_features=["stage2", "stage4"])
self.assertEqual(model.out_indices, [2, 4])
self.assertEqual(model.out_features, ["stage2", "stage4"])
def test_from_pretrained_identifier(self):
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(model, BertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_identifier_from_model_type(self):
model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
self.assertIsInstance(model, RobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
def test_from_pretrained_with_tuple_values(self):
# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
model = AutoModel.from_pretrained("sgugger/funnel-random-tiny")
self.assertIsInstance(model, FunnelModel)
config = copy.deepcopy(model.config)
config.architectures = ["FunnelBaseModel"]
model = AutoModel.from_config(config)
self.assertIsInstance(model, FunnelBaseModel)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
model = AutoModel.from_pretrained(tmp_dir)
self.assertIsInstance(model, FunnelBaseModel)
def test_from_pretrained_dynamic_model_local(self):
try:
AutoConfig.register("custom", CustomConfig)
AutoModel.register(CustomConfig, CustomModel)
config = CustomConfig(hidden_size=32)
model = CustomModel(config)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
for p1, p2 in zip(model.parameters(), new_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in MODEL_MAPPING._extra_content:
del MODEL_MAPPING._extra_content[CustomConfig]
def test_from_pretrained_dynamic_model_distant(self):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(ValueError):
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
# If remote code is disabled, we can't load this config.
with self.assertRaises(ValueError):
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(model.__class__.__name__, "NewModel")
# Test model can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# This one uses a relative import to a util file, this checks it is downloaded and used properly.
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_with_util", trust_remote_code=True)
self.assertEqual(model.__class__.__name__, "NewModel")
# Test model can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_from_pretrained_dynamic_model_distant_with_ref(self):
model = AutoModel.from_pretrained("hf-internal-testing/ref_to_test_dynamic_model", trust_remote_code=True)
self.assertEqual(model.__class__.__name__, "NewModel")
# Test model can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
# This one uses a relative import to a util file, this checks it is downloaded and used properly.
model = AutoModel.from_pretrained(
"hf-internal-testing/ref_to_test_dynamic_model_with_util", trust_remote_code=True
)
self.assertEqual(model.__class__.__name__, "NewModel")
# Test model can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True)
self.assertEqual(reloaded_model.__class__.__name__, "NewModel")
for p1, p2 in zip(model.parameters(), reloaded_model.parameters()):
self.assertTrue(torch.equal(p1, p2))
def test_new_model_registration(self):
AutoConfig.register("custom", CustomConfig)
auto_classes = [
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
]
try:
for auto_class in auto_classes:
with self.subTest(auto_class.__name__):
# Wrong config class will raise an error
with self.assertRaises(ValueError):
auto_class.register(BertConfig, CustomModel)
auto_class.register(CustomConfig, CustomModel)
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(ValueError):
auto_class.register(BertConfig, BertModel)
# Now that the config is registered, it can be used as any other config with the auto-API
tiny_config = BertModelTester(self).get_config()
config = CustomConfig(**tiny_config.to_dict())
model = auto_class.from_config(config)
self.assertIsInstance(model, CustomModel)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
new_model = auto_class.from_pretrained(tmp_dir)
# The model is a CustomModel but from the new dynamically imported class.
self.assertIsInstance(new_model, CustomModel)
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
for mapping in (
MODEL_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
):
if CustomConfig in mapping._extra_content:
del mapping._extra_content[CustomConfig]
def test_from_pretrained_dynamic_model_conflict(self):
class NewModelConfigLocal(BertConfig):
model_type = "new-model"
class NewModel(BertModel):
config_class = NewModelConfigLocal
try:
AutoConfig.register("new-model", NewModelConfigLocal)
AutoModel.register(NewModelConfigLocal, NewModel)
# If remote code is not set, the default is to use local
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model")
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
# If remote code is disabled, we load the local one.
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=False)
self.assertEqual(model.config.__class__.__name__, "NewModelConfigLocal")
# If remote is enabled, we load from the Hub
model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True)
self.assertEqual(model.config.__class__.__name__, "NewModelConfig")
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
if NewModelConfigLocal in MODEL_MAPPING._extra_content:
del MODEL_MAPPING._extra_content[NewModelConfigLocal]
def test_repo_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
):
_ = AutoModel.from_pretrained("bert-base")
def test_revision_not_found(self):
with self.assertRaisesRegex(
EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
):
_ = AutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
def test_model_file_not_found(self):
with self.assertRaisesRegex(
EnvironmentError,
"hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin",
):
_ = AutoModel.from_pretrained("hf-internal-testing/config-no-model")
def test_model_from_tf_suggestion(self):
with self.assertRaisesRegex(EnvironmentError, "Use `from_tf=True` to load this model"):
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-tf-only")
def test_model_from_flax_suggestion(self):
with self.assertRaisesRegex(EnvironmentError, "Use `from_flax=True` to load this model"):
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
def test_cached_model_has_minimum_calls_to_head(self):
# Make sure we have cached the model.
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
with RequestCounter() as counter:
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert")
self.assertEqual(counter.get_request_count, 0)
self.assertEqual(counter.head_request_count, 1)
self.assertEqual(counter.other_request_count, 0)
# With a sharded checkpoint
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
with RequestCounter() as counter:
_ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
self.assertEqual(counter.get_request_count, 0)
self.assertEqual(counter.head_request_count, 1)
self.assertEqual(counter.other_request_count, 0)
def test_attr_not_existing(self):
from transformers.models.auto.auto_factory import _LazyAutoMapping
_CONFIG_MAPPING_NAMES = OrderedDict([("bert", "BertConfig")])
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "GhostModel")])
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
with pytest.raises(ValueError, match=r"Could not find GhostModel neither in .* nor in .*!"):
_MODEL_MAPPING[BertConfig]
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "BertModel")])
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
self.assertEqual(_MODEL_MAPPING[BertConfig], BertModel)
_MODEL_MAPPING_NAMES = OrderedDict([("bert", "GPT2Model")])
_MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES)
self.assertEqual(_MODEL_MAPPING[BertConfig], GPT2Model)
| 23,735 | 44.734104 | 119 | py |
transformers | transformers-main/tests/models/nezha/test_modeling_nezha.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class NezhaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=128,
max_relative_position=32,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
"""
Returns a tiny configuration by default.
"""
return NezhaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = NezhaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = NezhaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = NezhaForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = NezhaForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = NezhaForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = NezhaForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = NezhaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = NezhaForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = NezhaForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class NezhaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": NezhaModel,
"fill-mask": NezhaForMaskedLM,
"question-answering": NezhaForQuestionAnswering,
"text-classification": NezhaForSequenceClassification,
"token-classification": NezhaForTokenClassification,
"zero-shot": NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = NezhaModelTester(self)
self.config_tester = ConfigTester(self, config_class=NezhaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = NezhaModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
@require_torch_gpu
def test_torchscript_device_change(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
config.torchscript = True
model = model_class(config=config)
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
traced_model = torch.jit.trace(
model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
)
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(traced_model, os.path.join(tmp, "bert.pt"))
loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device)
loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
@require_torch
class NezhaModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_nezha_model(self):
model = NezhaModel.from_pretrained("sijunhe/nezha-cn-base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
@slow
def test_inference_nezha_masked_lm(self):
model = NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 6, 21128))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
| 19,699 | 39.1222 | 119 | py |
transformers | transformers-main/tests/models/roberta/test_modeling_flax_roberta.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class FlaxRobertaModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = RobertaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class FlaxRobertaModelTest(FlaxModelTesterMixin, unittest.TestCase):
test_head_masking = True
all_model_classes = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def setUp(self):
self.model_tester = FlaxRobertaModelTester(self)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("roberta-base", from_pt=True)
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
| 5,809 | 35.3125 | 114 | py |
transformers | transformers-main/tests/models/roberta/test_modeling_roberta.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import RobertaConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
)
from transformers.models.roberta.modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaEmbeddings,
create_position_ids_from_input_ids,
)
ROBERTA_TINY = "sshleifer/tiny-distilroberta-base"
class RobertaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return RobertaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RobertaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RobertaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = RobertaForCausalLM(config=config).to(torch_device).eval()
# make sure that ids don't start with pad token
mask = input_ids.ne(config.pad_token_id).long()
input_ids = input_ids * mask
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
# make sure that ids don't start with pad token
mask = next_tokens.ne(config.pad_token_id).long()
next_tokens = next_tokens * mask
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RobertaForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = RobertaForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class RobertaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaModel,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (RobertaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": RobertaModel,
"fill-mask": RobertaForMaskedLM,
"question-answering": RobertaForQuestionAnswering,
"text-classification": RobertaForSequenceClassification,
"text-generation": RobertaForCausalLM,
"token-classification": RobertaForTokenClassification,
"zero-shot": RobertaForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
def setUp(self):
self.model_tester = RobertaModelTester(self)
self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
config_and_inputs[0].position_embedding_type = "relative_key"
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = RobertaModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_create_position_ids_respects_padding_index(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is RobertaEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
model = RobertaEmbeddings(config=config)
input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
expected_positions = torch.as_tensor(
[[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]]
)
position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
def test_create_position_ids_from_inputs_embeds(self):
"""Ensure that the default position ids only assign a sequential . This is a regression
test for https://github.com/huggingface/transformers/issues/1761
The position ids should be masked with the embedding object's padding index. Therefore, the
first available non-padding position index is RobertaEmbeddings.padding_idx + 1
"""
config = self.model_tester.prepare_config_and_inputs()[0]
embeddings = RobertaEmbeddings(config=config)
inputs_embeds = torch.empty(2, 4, 30)
expected_single_positions = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
self.assertEqual(position_ids.shape, expected_positions.shape)
self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))
@require_torch
class RobertaModelIntegrationTest(TestCasePlus):
@slow
def test_inference_masked_lm(self):
model = RobertaForMaskedLM.from_pretrained("roberta-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
)
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
# roberta.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_no_head(self):
model = RobertaModel.from_pretrained("roberta-base")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
)
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
# roberta.eval()
# expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_classification_head(self):
model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3))
self.assertEqual(output.shape, expected_shape)
expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
# roberta.eval()
# expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()
self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))
| 23,228 | 38.981067 | 117 | py |
transformers | transformers-main/tests/models/open_llama/test_modeling_open_llama.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Open-Llama model. """
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class OpenLlamaModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return OpenLlamaConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
use_stable_embedding=False,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = OpenLlamaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = OpenLlamaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = OpenLlamaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = OpenLlamaForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class OpenLlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
all_generative_model_classes = (OpenLlamaForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = OpenLlamaModelTester(self)
self.config_tester = ConfigTester(self, config_class=OpenLlamaConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_open_llama_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = OpenLlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_open_llama_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = OpenLlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_open_llama_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
).to(torch.float)
model = OpenLlamaForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
@unittest.skip("Open-Llama buffers include complex numbers, which breaks this test")
def test_save_load_fast_init_from_base(self):
pass
@parameterized.expand([("linear",), ("dynamic",)])
def test_model_rope_scaling(self, scaling_type):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
short_input = ids_tensor([1, 10], config.vocab_size)
long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size)
set_seed(42) # Fixed seed at init time so the two models get the same random weights
original_model = OpenLlamaModel(config)
original_model.to(torch_device)
original_model.eval()
original_short_output = original_model(short_input).last_hidden_state
original_long_output = original_model(long_input).last_hidden_state
set_seed(42) # Fixed seed at init time so the two models get the same random weights
config.rope_scaling = {"type": scaling_type, "factor": 10.0}
scaled_model = OpenLlamaModel(config)
scaled_model.to(torch_device)
scaled_model.eval()
scaled_short_output = scaled_model(short_input).last_hidden_state
scaled_long_output = scaled_model(long_input).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
else:
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
# The output should be different for long inputs
self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5))
| 15,136 | 39.800539 | 117 | py |
transformers | transformers-main/tests/models/visual_bert/test_modeling_visual_bert.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch VisualBERT model. """
import copy
import unittest
from transformers import VisualBertConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForRegionToPhraseAlignment,
VisualBertForVisualReasoning,
VisualBertModel,
)
from transformers.models.visual_bert.modeling_visual_bert import VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
class VisualBertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
visual_seq_length=5,
is_training=True,
use_attention_mask=True,
use_visual_attention_mask=True,
use_token_type_ids=True,
use_visual_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
visual_embedding_dim=20,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.visual_seq_length = visual_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_visual_attention_mask = use_visual_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_visual_token_type_ids = use_visual_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.visual_embedding_dim = visual_embedding_dim
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def get_config(self):
return VisualBertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
visual_embedding_dim=self.visual_embedding_dim,
num_labels=self.num_labels,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
visual_embeds = floats_tensor([self.batch_size, self.visual_seq_length, self.visual_embedding_dim])
attention_mask = None
if self.use_attention_mask:
attention_mask = torch.ones((self.batch_size, self.seq_length), dtype=torch.long, device=torch_device)
visual_attention_mask = None
if self.use_visual_attention_mask:
visual_attention_mask = torch.ones(
(self.batch_size, self.visual_seq_length), dtype=torch.long, device=torch_device
)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
visual_token_type_ids = None
if self.use_visual_token_type_ids:
visual_token_type_ids = ids_tensor([self.batch_size, self.visual_seq_length], self.type_vocab_size)
config = self.get_config()
return config, {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
def prepare_config_and_inputs_for_pretraining(self):
masked_lm_labels = None
sentence_image_labels = None
if self.use_labels:
masked_lm_labels = ids_tensor([self.batch_size, self.seq_length + self.visual_seq_length], self.vocab_size)
sentence_image_labels = ids_tensor(
[self.batch_size],
self.type_sequence_label_size,
)
config, input_dict = self.prepare_config_and_inputs_for_common()
input_dict.update({"labels": masked_lm_labels, "sentence_image_labels": sentence_image_labels})
return config, input_dict
def prepare_config_and_inputs_for_multiple_choice(self):
input_ids = ids_tensor([self.batch_size, self.num_choices, self.seq_length], self.vocab_size)
visual_embeds = floats_tensor(
[self.batch_size, self.num_choices, self.visual_seq_length, self.visual_embedding_dim]
)
attention_mask = None
if self.use_attention_mask:
attention_mask = torch.ones(
(self.batch_size, self.num_choices, self.seq_length), dtype=torch.long, device=torch_device
)
visual_attention_mask = None
if self.use_visual_attention_mask:
visual_attention_mask = torch.ones(
(self.batch_size, self.num_choices, self.visual_seq_length), dtype=torch.long, device=torch_device
)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.num_choices, self.seq_length], self.type_vocab_size)
visual_token_type_ids = None
if self.use_visual_token_type_ids:
visual_token_type_ids = ids_tensor(
[self.batch_size, self.num_choices, self.visual_seq_length], self.type_vocab_size
)
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
"labels": labels,
}
def prepare_config_and_inputs_for_vqa(self):
vqa_labels = None
if self.use_labels:
vqa_labels = floats_tensor([self.batch_size, self.num_labels])
config, input_dict = self.prepare_config_and_inputs_for_common()
input_dict.update({"labels": vqa_labels})
return config, input_dict
def prepare_config_and_inputs_for_nlvr(self):
nlvr_labels = None
if self.use_labels:
nlvr_labels = ids_tensor([self.batch_size], self.num_labels)
config, input_dict = self.prepare_config_and_inputs_for_common()
input_dict.update({"labels": nlvr_labels})
return config, input_dict
def prepare_config_and_inputs_for_flickr(self):
region_to_phrase_position = torch.cat(
(
ids_tensor([self.batch_size, self.seq_length], self.visual_seq_length),
torch.ones(self.batch_size, self.visual_seq_length, dtype=torch.long, device=torch_device) * -1,
),
dim=-1,
)
flickr_labels = None
if self.use_labels:
flickr_labels = floats_tensor(
[self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length]
)
config, input_dict = self.prepare_config_and_inputs_for_common()
input_dict.update({"region_to_phrase_position": region_to_phrase_position, "labels": flickr_labels})
return config, input_dict
def create_and_check_model(self, config, input_dict):
model = VisualBertModel(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length + self.visual_seq_length, self.hidden_size),
)
def create_and_check_for_pretraining(self, config, input_dict):
model = VisualBertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(
result.prediction_logits.shape,
(self.batch_size, self.seq_length + self.visual_seq_length, self.vocab_size),
)
def create_and_check_for_vqa(self, config, input_dict):
model = VisualBertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_multiple_choice(self, config, input_dict):
model = VisualBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_for_nlvr(self, config, input_dict):
model = VisualBertForVisualReasoning(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_flickr(self, config, input_dict):
model = VisualBertForRegionToPhraseAlignment(config=config)
model.to(torch_device)
model.eval()
result = model(**input_dict)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length)
)
@require_torch
class VisualBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
VisualBertModel,
VisualBertForMultipleChoice,
VisualBertForVisualReasoning,
VisualBertForRegionToPhraseAlignment,
VisualBertForQuestionAnswering,
VisualBertForPreTraining,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = {"feature-extraction": VisualBertModel} if is_torch_available() else {}
test_torchscript = False
test_pruning = False
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = copy.deepcopy(inputs_dict)
if model_class == VisualBertForMultipleChoice:
for key in inputs_dict.keys():
value = inputs_dict[key]
if isinstance(value, torch.Tensor) and value.ndim > 1:
if key != "visual_embeds":
inputs_dict[key] = (
inputs_dict[key].unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
)
else:
inputs_dict[key] = (
inputs_dict[key]
.unsqueeze(1)
.expand(-1, self.model_tester.num_choices, -1, self.model_tester.visual_embedding_dim)
.contiguous()
)
elif model_class == VisualBertForRegionToPhraseAlignment:
total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
batch_size = self.model_tester.batch_size
inputs_dict["region_to_phrase_position"] = torch.zeros(
(batch_size, total_length),
dtype=torch.long,
device=torch_device,
)
if return_labels:
if model_class == VisualBertForMultipleChoice:
inputs_dict["labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
elif model_class == VisualBertForPreTraining:
total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
batch_size = self.model_tester.batch_size
inputs_dict["labels"] = torch.zeros(
(batch_size, total_length),
dtype=torch.long,
device=torch_device,
)
inputs_dict["sentence_image_labels"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
# Flickr expects float labels
elif model_class == VisualBertForRegionToPhraseAlignment:
batch_size = self.model_tester.batch_size
total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
inputs_dict["labels"] = torch.ones(
(
batch_size,
total_length,
self.model_tester.visual_seq_length,
),
dtype=torch.float,
device=torch_device,
)
# VQA expects float labels
elif model_class == VisualBertForQuestionAnswering:
inputs_dict["labels"] = torch.ones(
(self.model_tester.batch_size, self.model_tester.num_labels),
dtype=torch.float,
device=torch_device,
)
elif model_class == VisualBertForVisualReasoning:
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size), dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = VisualBertModelTester(self)
self.config_tester = ConfigTester(self, config_class=VisualBertConfig, hidden_size=37)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
visual_seq_len = getattr(self.model_tester, "visual_seq_length", None)
encoder_seq_length = (seq_len if seq_len is not None else 0) + (
visual_seq_len if visual_seq_len is not None else 0
)
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
chunk_length = getattr(self.model_tester, "chunk_length", None)
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
elif self.is_encoder_decoder:
added_hidden_states = 2
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
if chunk_length is not None:
self.assertListEqual(
list(self_attentions[0].shape[-4:]),
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
)
else:
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
if hasattr(self.model_tester, "encoder_seq_length"):
seq_length = self.model_tester.encoder_seq_length
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
seq_length = seq_length * self.model_tester.chunk_length
else:
seq_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_model_for_vqa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_vqa()
self.model_tester.create_and_check_for_vqa(*config_and_inputs)
def test_model_for_nlvr(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_nlvr()
self.model_tester.create_and_check_for_nlvr(*config_and_inputs)
def test_model_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_multiple_choice()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_model_for_flickr(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_flickr()
self.model_tester.create_and_check_for_flickr(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = VisualBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class VisualBertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_vqa_coco_pre(self):
model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
with torch.no_grad():
output = model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
)
vocab_size = 30522
expected_shape = torch.Size((1, 16, vocab_size))
self.assertEqual(output.prediction_logits.shape, expected_shape)
expected_slice = torch.tensor(
[[[-5.1858, -5.1903, -4.9142], [-6.2214, -5.9238, -5.8381], [-6.3027, -5.9939, -5.9297]]]
)
self.assertTrue(torch.allclose(output.prediction_logits[:, :3, :3], expected_slice, atol=1e-4))
expected_shape_2 = torch.Size((1, 2))
self.assertEqual(output.seq_relationship_logits.shape, expected_shape_2)
expected_slice_2 = torch.tensor([[0.7393, 0.1754]])
self.assertTrue(torch.allclose(output.seq_relationship_logits, expected_slice_2, atol=1e-4))
@slow
def test_inference_vqa(self):
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa")
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
with torch.no_grad():
output = model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
)
# vocab_size = 30522
expected_shape = torch.Size((1, 3129))
self.assertEqual(output.logits.shape, expected_shape)
expected_slice = torch.tensor(
[[-8.9898, 3.0803, -1.8016, 2.4542, -8.3420, -2.0224, -3.3124, -4.4139, -3.1491, -3.8997]]
)
self.assertTrue(torch.allclose(output.logits[:, :10], expected_slice, atol=1e-4))
@slow
def test_inference_nlvr(self):
model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2")
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
visual_embeds = torch.ones(size=(1, 10, 1024), dtype=torch.float32) * 0.5
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
with torch.no_grad():
output = model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
)
# vocab_size = 30522
expected_shape = torch.Size((1, 2))
self.assertEqual(output.logits.shape, expected_shape)
expected_slice = torch.tensor([[-1.1436, 0.8900]])
self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4))
@slow
def test_inference_vcr(self):
model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr")
input_ids = torch.tensor([[[1, 2, 3, 4, 5, 6] for i in range(4)]], dtype=torch.long)
attention_mask = torch.ones_like(input_ids)
token_type_ids = torch.ones_like(input_ids)
visual_embeds = torch.ones(size=(1, 4, 10, 512), dtype=torch.float32) * 0.5
visual_token_type_ids = torch.ones(size=(1, 4, 10), dtype=torch.long)
visual_attention_mask = torch.ones_like(visual_token_type_ids)
with torch.no_grad():
output = model(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
visual_embeds=visual_embeds,
visual_attention_mask=visual_attention_mask,
visual_token_type_ids=visual_token_type_ids,
)
# vocab_size = 30522
expected_shape = torch.Size((1, 4))
self.assertEqual(output.logits.shape, expected_shape)
expected_slice = torch.tensor([[-7.7697, -7.7697, -7.7697, -7.7697]])
self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4))
| 28,892 | 40.75289 | 119 | py |
transformers | transformers-main/tests/models/falcon/test_modeling_falcon.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Falcon model. """
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class FalconModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return FalconConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
pad_token_id=1,
new_decoder_architecture=True,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = FalconModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = FalconModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = FalconForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = FalconForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (FalconForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": FalconModel,
"text-classification": FalconForSequenceClassification,
"text-generation": FalconForCausalLM,
"question-answering": FalconForQuestionAnswering,
"token-classification": FalconForTokenClassification,
"zero-shot": FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
def setUp(self):
self.model_tester = FalconModelTester(self)
self.config_tester = ConfigTester(self, config_class=FalconConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_position_embedding_types(self):
config, *inputs = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
config.alibi = alibi
self.model_tester.create_and_check_model(config, *inputs)
def test_falcon_sequence_classification_model(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_falcon_sequence_classification_model_for_single_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "single_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_cache_conversions(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_ids = input_dict["input_ids"]
model = FalconForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, use_cache=True)
batch_size = input_ids.shape[0]
rw_cache = model._convert_to_rw_cache(result.past_key_values)
standard_cache = model._convert_cache_to_standard_format(rw_cache, batch_size)
for layer in range(len(rw_cache)):
for tensor_idx in range(2):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx])
)
def test_falcon_sequence_classification_model_for_multi_label(self):
config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.num_labels = 3
config.problem_type = "multi_label_classification"
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
sequence_labels = ids_tensor(
[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
).to(torch.float)
model = FalconForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
def test_past_key_values_format(self):
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(config, "use_cache"):
return
model = model_class(config).to(torch_device)
if "use_cache" not in inputs:
inputs["use_cache"] = True
outputs = model(**inputs)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
num_hidden_layers = (
getattr(config, "decoder_layers", None)
or getattr(config, "num_decoder_layers", None)
or config.num_hidden_layers
)
num_attention_heads = getattr(config, "num_kv_heads", config.num_attention_heads)
embed_dim = getattr(config, "d_model", config.hidden_size)
per_head_embed_dim = embed_dim // num_attention_heads
past_kv = outputs["past_key_values"]
self.assertEqual(len(past_kv), num_hidden_layers)
batch_size, seq_length = inputs["input_ids"].shape
for i in range(num_hidden_layers):
if config.new_decoder_architecture:
num_attention_heads = config.num_attention_heads
elif config.multi_query:
num_attention_heads = 1
self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
self.assertEqual(
past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim)
)
@require_torch
class FalconLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_falcon(self):
tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b")
model = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b")
model.eval()
model.to(torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
EXPECTED_OUTPUT = (
"My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
)
output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=19)
output_str = tokenizer.batch_decode(output_ids)[0]
self.assertEqual(output_str, EXPECTED_OUTPUT)
@slow
def test_lm_generation_big_models(self):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
tokenizer = AutoTokenizer.from_pretrained(repo)
model = FalconForCausalLM.from_pretrained(repo)
model.eval()
model.to(torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**inputs, do_sample=False, max_new_tokens=4)
model.generate(**inputs, do_sample=True, max_new_tokens=4)
model.generate(**inputs, num_beams=2, max_new_tokens=4)
@slow
def test_lm_generation_use_cache(self):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
tokenizer = AutoTokenizer.from_pretrained(repo)
model = FalconForCausalLM.from_pretrained(repo)
model.eval()
model.to(device=torch_device)
inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device)
# Test results are the same with and without cache
outputs_no_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False)
outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True)
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)
| 19,221 | 39.897872 | 117 | py |
transformers | transformers-main/tests/models/owlvit/test_processor_owlvit.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor, OwlViTProcessor
@require_vision
class OwlViTProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
# fmt: off
vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
image_processor_map = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_std": [0.26862954, 0.26130258, 0.27577711],
}
self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME)
with open(self.image_processor_file, "w", encoding="utf-8") as fp:
json.dump(image_processor_map, fp)
def get_tokenizer(self, **kwargs):
return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
def get_rust_tokenizer(self, **kwargs):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
def get_image_processor(self, **kwargs):
return OwlViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_default(self):
tokenizer_slow = self.get_tokenizer()
tokenizer_fast = self.get_rust_tokenizer()
image_processor = self.get_image_processor()
processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, image_processor=image_processor)
processor_slow.save_pretrained(self.tmpdirname)
processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False)
processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, image_processor=image_processor)
processor_fast.save_pretrained(self.tmpdirname)
processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor, OwlViTImageProcessor)
self.assertIsInstance(processor_fast.image_processor, OwlViTImageProcessor)
def test_save_load_pretrained_additional_features(self):
processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False)
processor = OwlViTProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, OwlViTImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_image_proc = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str, return_tensors="np")
encoded_tok = tokenizer(input_str, return_tensors="np")
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist())
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_processor_with_text_list(self):
model_name = "google/owlvit-base-patch32"
processor = OwlViTProcessor.from_pretrained(model_name)
input_text = ["cat", "nasa badge"]
inputs = processor(text=input_text)
seq_length = 16
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_processor_with_nested_text_list(self):
model_name = "google/owlvit-base-patch32"
processor = OwlViTProcessor.from_pretrained(model_name)
input_texts = [["cat", "nasa badge"], ["person"]]
inputs = processor(text=input_texts)
seq_length = 16
batch_size = len(input_texts)
num_max_text_queries = max([len(texts) for texts in input_texts])
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length))
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_processor_case(self):
model_name = "google/owlvit-base-patch32"
processor = OwlViTProcessor.from_pretrained(model_name)
input_texts = ["cat", "nasa badge"]
inputs = processor(text=input_texts)
seq_length = 16
input_ids = inputs["input_ids"]
predicted_ids = [
[49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
self.assertListEqual(list(input_ids[0]), predicted_ids[0])
self.assertListEqual(list(input_ids[1]), predicted_ids[1])
def test_processor_case2(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
query_input = self.prepare_image_inputs()
inputs = processor(images=image_input, query_images=query_input)
self.assertListEqual(list(inputs.keys()), ["query_pixel_values", "pixel_values"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
| 10,665 | 40.181467 | 214 | py |
transformers | transformers-main/tests/models/owlvit/test_image_processing_owlvit.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import OwlViTImageProcessor
class OwlViTImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_center_crop=True,
crop_size=None,
do_normalize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
do_convert_rgb=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size if size is not None else {"height": 18, "width": 18}
self.do_center_crop = do_center_crop
self.crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
@require_torch
@require_vision
class OwlViTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = OwlViTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = OwlViTImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_center_crop"))
self.assertTrue(hasattr(image_processing, "center_crop"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"height": 18, "width": 18})
self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {"height": 42, "width": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
| 8,062 | 37.395238 | 113 | py |
transformers | transformers-main/tests/models/owlvit/test_modeling_owlvit.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch OwlViT model. """
import inspect
import os
import tempfile
import unittest
import numpy as np
import requests
from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig
from transformers.testing_utils import require_torch, require_torch_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel
from transformers.models.owlvit.modeling_owlvit import OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import OwlViTProcessor
class OwlViTVisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=32,
patch_size=2,
num_channels=3,
is_training=True,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return OwlViTVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = OwlViTVisionModel(config=config).to(torch_device)
model.eval()
pixel_values = pixel_values.to(torch.float32)
with torch.no_grad():
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
num_patches = (self.image_size // self.patch_size) ** 2
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as OWLVIT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (OwlViTVisionModel,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = OwlViTVisionModelTester(self)
self.config_tester = ConfigTester(
self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=37
)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="OWLVIT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OwlViTVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class OwlViTTextModelTester:
def __init__(
self,
parent,
batch_size=12,
num_queries=4,
seq_length=16,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=64,
num_hidden_layers=12,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
max_position_embeddings=16,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.num_queries = num_queries
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length])
if input_mask is not None:
num_text, seq_length = input_mask.shape
rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,))
for idx, start_index in enumerate(rnd_start_indices):
input_mask[idx, :start_index] = 1
input_mask[idx, start_index:] = 0
config = self.get_config()
return config, input_ids, input_mask
def get_config(self):
return OwlViTTextConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, input_ids, input_mask):
model = OwlViTTextModel(config=config).to(torch_device)
model.eval()
with torch.no_grad():
result = model(input_ids=input_ids, attention_mask=input_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size)
)
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, input_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (OwlViTTextModel,) if is_torch_available() else ()
fx_compatible = False
test_pruning = False
test_head_masking = False
def setUp(self):
self.model_tester = OwlViTTextModelTester(self)
self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="OWLVIT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OwlViTTextModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class OwlViTModelTester:
def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
if text_kwargs is None:
text_kwargs = {}
if vision_kwargs is None:
vision_kwargs = {}
self.parent = parent
self.text_model_tester = OwlViTTextModelTester(parent, **text_kwargs)
self.vision_model_tester = OwlViTVisionModelTester(parent, **vision_kwargs)
self.is_training = is_training
self.text_config = self.text_model_tester.get_config().to_dict()
self.vision_config = self.vision_model_tester.get_config().to_dict()
def prepare_config_and_inputs(self):
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
config = self.get_config()
return config, input_ids, attention_mask, pixel_values
def get_config(self):
return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
model = OwlViTModel(config).to(torch_device).eval()
with torch.no_grad():
result = model(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
)
image_logits_size = (
self.vision_model_tester.batch_size,
self.text_model_tester.batch_size * self.text_model_tester.num_queries,
)
text_logits_size = (
self.text_model_tester.batch_size * self.text_model_tester.num_queries,
self.vision_model_tester.batch_size,
)
self.parent.assertEqual(result.logits_per_image.shape, image_logits_size)
self.parent.assertEqual(result.logits_per_text.shape, text_logits_size)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask, pixel_values = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"return_loss": False,
}
return config, inputs_dict
@require_torch
class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (OwlViTModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection}
if is_torch_available()
else {}
)
fx_compatible = False
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
def setUp(self):
self.model_tester = OwlViTModelTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="OwlViTModel does not have input/output embeddings")
def test_model_common_attributes(self):
pass
# override as the `logit_scale` parameter initilization is different for OWLVIT
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
# check if `logit_scale` is initilized as per the original implementation
if name == "logit_scale":
self.assertAlmostEqual(
param.data.item(),
np.log(1 / 0.07),
delta=1e-3,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
configs_no_init.return_dict = False
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init).to(torch_device)
model.eval()
try:
input_ids = inputs_dict["input_ids"]
pixel_values = inputs_dict["pixel_values"] # OWLVIT needs pixel_values
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
loaded_model = loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_load_vision_text_config(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Save OwlViTConfig and check if we can load OwlViTVisionConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
# Save OwlViTConfig and check if we can load OwlViTTextConfig from it
with tempfile.TemporaryDirectory() as tmp_dir_name:
config.save_pretrained(tmp_dir_name)
text_config = OwlViTTextConfig.from_pretrained(tmp_dir_name)
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
@slow
def test_model_from_pretrained(self):
for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OwlViTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class OwlViTForObjectDetectionTester:
def __init__(self, parent, is_training=True):
self.parent = parent
self.text_model_tester = OwlViTTextModelTester(parent)
self.vision_model_tester = OwlViTVisionModelTester(parent)
self.is_training = is_training
self.text_config = self.text_model_tester.get_config().to_dict()
self.vision_config = self.vision_model_tester.get_config().to_dict()
def prepare_config_and_inputs(self):
text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
config = self.get_config()
return config, pixel_values, input_ids, attention_mask
def get_config(self):
return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
def create_and_check_model(self, config, pixel_values, input_ids, attention_mask):
model = OwlViTForObjectDetection(config).to(torch_device).eval()
with torch.no_grad():
result = model(
pixel_values=pixel_values,
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=True,
)
pred_boxes_size = (
self.vision_model_tester.batch_size,
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
4,
)
pred_logits_size = (
self.vision_model_tester.batch_size,
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
4,
)
pred_class_embeds_size = (
self.vision_model_tester.batch_size,
(self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
self.text_model_tester.hidden_size,
)
self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size)
self.parent.assertEqual(result.logits.shape, pred_logits_size)
self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, input_ids, attention_mask = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (OwlViTForObjectDetection,) if is_torch_available() else ()
fx_compatible = False
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
test_attention_outputs = False
def setUp(self):
self.model_tester = OwlViTForObjectDetectionTester(self)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip(reason="Hidden_states is tested in individual model tests")
def test_hidden_states_output(self):
pass
@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Retain_grad is tested in individual model tests")
def test_retain_grad_hidden_states_attentions(self):
pass
@unittest.skip(reason="OwlViTModel does not have input/output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="Test_initialization is tested in individual model tests")
def test_initialization(self):
pass
@unittest.skip(reason="Test_forward_signature is tested in individual model tests")
def test_forward_signature(self):
pass
@unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training(self):
pass
@unittest.skip(reason="OWL-ViT does not support training yet")
def test_training_gradient_checkpointing(self):
pass
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
configs_no_init.return_dict = False
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init).to(torch_device)
model.eval()
try:
input_ids = inputs_dict["input_ids"]
pixel_values = inputs_dict["pixel_values"] # OWLVIT needs pixel_values
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
except RuntimeError:
self.fail("Couldn't trace module.")
with tempfile.TemporaryDirectory() as tmp_dir_name:
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
try:
torch.jit.save(traced_model, pt_file_name)
except Exception:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load(pt_file_name)
except Exception:
self.fail("Couldn't load module.")
loaded_model = loaded_model.to(torch_device)
loaded_model.eval()
model_state_dict = model.state_dict()
loaded_model_state_dict = loaded_model.state_dict()
non_persistent_buffers = {}
for key in loaded_model_state_dict.keys():
if key not in model_state_dict.keys():
non_persistent_buffers[key] = loaded_model_state_dict[key]
loaded_model_state_dict = {
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
}
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
model_buffers = list(model.buffers())
for non_persistent_buffer in non_persistent_buffers.values():
found_buffer = False
for i, model_buffer in enumerate(model_buffers):
if torch.equal(non_persistent_buffer, model_buffer):
found_buffer = True
break
self.assertTrue(found_buffer)
model_buffers.pop(i)
models_equal = True
for layer_name, p1 in model_state_dict.items():
p2 = loaded_model_state_dict[layer_name]
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
@slow
def test_model_from_pretrained(self):
for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = OwlViTForObjectDetection.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@require_vision
@require_torch
class OwlViTModelIntegrationTest(unittest.TestCase):
@slow
def test_inference(self):
model_name = "google/owlvit-base-patch32"
model = OwlViTModel.from_pretrained(model_name).to(torch_device)
processor = OwlViTProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(
text=[["a photo of a cat", "a photo of a dog"]],
images=image,
max_length=16,
padding="max_length",
return_tensors="pt",
).to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
)
self.assertEqual(
outputs.logits_per_text.shape,
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
)
expected_logits = torch.tensor([[3.4613, 0.9403]], device=torch_device)
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
@slow
def test_inference_object_detection(self):
model_name = "google/owlvit-base-patch32"
model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
processor = OwlViTProcessor.from_pretrained(model_name)
image = prepare_img()
inputs = processor(
text=[["a photo of a cat", "a photo of a dog"]],
images=image,
max_length=16,
padding="max_length",
return_tensors="pt",
).to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
expected_slice_boxes = torch.tensor(
[[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
@slow
def test_inference_one_shot_object_detection(self):
model_name = "google/owlvit-base-patch32"
model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
processor = OwlViTProcessor.from_pretrained(model_name)
image = prepare_img()
query_image = prepare_img()
inputs = processor(
images=image,
query_images=query_image,
max_length=16,
padding="max_length",
return_tensors="pt",
).to(torch_device)
with torch.no_grad():
outputs = model.image_guided_detection(**inputs)
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
expected_slice_boxes = torch.tensor(
[[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
@slow
@require_torch_gpu
def test_inference_one_shot_object_detection_fp16(self):
model_name = "google/owlvit-base-patch32"
model = OwlViTForObjectDetection.from_pretrained(model_name, torch_dtype=torch.float16).to(torch_device)
processor = OwlViTProcessor.from_pretrained(model_name)
image = prepare_img()
query_image = prepare_img()
inputs = processor(
images=image,
query_images=query_image,
max_length=16,
padding="max_length",
return_tensors="pt",
).to(torch_device)
with torch.no_grad():
outputs = model.image_guided_detection(**inputs)
# No need to check the logits, we just check inference runs fine.
num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
| 32,644 | 37.405882 | 121 | py |
transformers | transformers-main/tests/models/autoformer/test_modeling_autoformer.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Autoformer model. """
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
TOLERANCE = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class AutoformerModelTester:
def __init__(
self,
parent,
d_model=16,
batch_size=13,
prediction_length=7,
context_length=14,
label_length=10,
cardinality=19,
embedding_dimension=5,
num_time_features=4,
is_training=True,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
lags_sequence=[1, 2, 3, 4, 5],
moving_average=25,
autocorrelation_factor=5,
):
self.d_model = d_model
self.parent = parent
self.batch_size = batch_size
self.prediction_length = prediction_length
self.context_length = context_length
self.cardinality = cardinality
self.num_time_features = num_time_features
self.lags_sequence = lags_sequence
self.embedding_dimension = embedding_dimension
self.is_training = is_training
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.encoder_seq_length = context_length
self.decoder_seq_length = prediction_length + label_length
self.label_length = label_length
self.moving_average = moving_average
self.autocorrelation_factor = autocorrelation_factor
def get_config(self):
return AutoformerConfig(
d_model=self.d_model,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
prediction_length=self.prediction_length,
context_length=self.context_length,
label_length=self.label_length,
lags_sequence=self.lags_sequence,
num_time_features=self.num_time_features,
num_static_categorical_features=1,
cardinality=[self.cardinality],
embedding_dimension=[self.embedding_dimension],
moving_average=self.moving_average,
)
def prepare_autoformer_inputs_dict(self, config):
_past_length = config.context_length + max(config.lags_sequence)
static_categorical_features = ids_tensor([self.batch_size, 1], config.cardinality[0])
past_time_features = floats_tensor([self.batch_size, _past_length, config.num_time_features])
past_values = floats_tensor([self.batch_size, _past_length])
past_observed_mask = floats_tensor([self.batch_size, _past_length]) > 0.5
# decoder inputs
future_time_features = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
future_values = floats_tensor([self.batch_size, config.prediction_length])
inputs_dict = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def prepare_config_and_inputs(self):
config = self.get_config()
inputs_dict = self.prepare_autoformer_inputs_dict(config)
return config, inputs_dict
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = AutoformerModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = AutoformerEncoder.from_pretrained(tmpdirname).to(torch_device)
transformer_inputs, feature, _, _, _ = model.create_network_inputs(**inputs_dict)
seasonal_input, trend_input = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])
enc_input = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]),
dim=-1,
)
encoder_last_hidden_state_2 = encoder(inputs_embeds=enc_input)[0]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
mean = (
torch.mean(transformer_inputs[:, : config.context_length, ...], dim=1)
.unsqueeze(1)
.repeat(1, config.prediction_length, 1)
)
zeros = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]],
device=enc_input.device,
)
dec_input = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros), dim=1),
feature[:, config.context_length - config.label_length :, ...],
),
dim=-1,
)
trend_init = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean), dim=1),
feature[:, config.context_length - config.label_length :, ...],
),
dim=-1,
)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = AutoformerDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
trend=trend_init,
inputs_embeds=dec_input,
encoder_hidden_states=encoder_last_hidden_state,
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class AutoformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
all_generative_model_classes = (AutoformerForPrediction,) if is_torch_available() else ()
pipeline_model_mapping = {"feature-extraction": AutoformerModel} if is_torch_available() else {}
test_pruning = False
test_head_masking = False
test_missing_keys = False
test_torchscript = False
test_inputs_embeds = False
test_model_common_attributes = False
def setUp(self):
self.model_tester = AutoformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=AutoformerConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
@unittest.skip(reason="Model has no tokens embeddings")
def test_resize_tokens_embeddings(self):
pass
# # Input is 'static_categorical_features' not 'input_ids'
def test_model_main_input_name(self):
model_signature = inspect.signature(getattr(AutoformerModel, "forward"))
# The main input is the name of the argument after `self`
observed_main_input_name = list(model_signature.parameters.keys())[1]
self.assertEqual(AutoformerModel.main_input_name, observed_main_input_name)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask")
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
]
)
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
d_model = getattr(self.model_tester, "d_model", None)
num_attention_heads = getattr(self.model_tester, "num_attention_heads", None)
dim = d_model // num_attention_heads
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.encoder_attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, dim],
)
out_len = len(outputs)
correct_outlen = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(out_len, correct_outlen)
# decoder attentions
decoder_attentions = outputs.decoder_attentions
self.assertIsInstance(decoder_attentions, (list, tuple))
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(decoder_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, dim],
)
# cross attentions
cross_attentions = outputs.cross_attentions
self.assertIsInstance(cross_attentions, (list, tuple))
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(cross_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, decoder_seq_length, dim],
)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 2, len(outputs))
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, encoder_seq_length, dim],
)
@is_flaky()
def test_retain_grad_hidden_states_attentions(self):
super().test_retain_grad_hidden_states_attentions()
def prepare_batch(filename="train-batch.pt"):
file = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch", filename=filename, repo_type="dataset")
batch = torch.load(file, map_location=torch_device)
return batch
@require_torch
@slow
class AutoformerModelIntegrationTests(unittest.TestCase):
def test_inference_no_head(self):
model = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device)
batch = prepare_batch()
with torch.no_grad():
output = model(
past_values=batch["past_values"],
past_time_features=batch["past_time_features"],
past_observed_mask=batch["past_observed_mask"],
static_categorical_features=batch["static_categorical_features"],
future_values=batch["future_values"],
future_time_features=batch["future_time_features"],
)[0]
expected_shape = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size)
)
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]], device=torch_device
)
self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_head(self):
model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device)
batch = prepare_batch("val-batch.pt")
with torch.no_grad():
output = model(
past_values=batch["past_values"],
past_time_features=batch["past_time_features"],
past_observed_mask=batch["past_observed_mask"],
static_categorical_features=batch["static_categorical_features"],
).encoder_last_hidden_state
expected_shape = torch.Size((64, model.config.context_length, model.config.d_model))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]], device=torch_device
)
self.assertTrue(torch.allclose(output[0, :3, :3], expected_slice, atol=TOLERANCE))
def test_seq_to_seq_generation(self):
model = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly").to(torch_device)
batch = prepare_batch("val-batch.pt")
with torch.no_grad():
outputs = model.generate(
static_categorical_features=batch["static_categorical_features"],
past_time_features=batch["past_time_features"],
past_values=batch["past_values"],
future_time_features=batch["future_time_features"],
past_observed_mask=batch["past_observed_mask"],
)
expected_shape = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
self.assertEqual(outputs.sequences.shape, expected_shape)
expected_slice = torch.tensor([3130.6763, 4056.5293, 7053.0786], device=torch_device)
mean_prediction = outputs.sequences.mean(dim=1)
self.assertTrue(torch.allclose(mean_prediction[0, -3:], expected_slice, rtol=1e-1))
| 19,087 | 40.859649 | 119 | py |
transformers | transformers-main/tests/models/glpn/test_modeling_glpn.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch GLPN model. """
import inspect
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class GLPNConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
class GLPNModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
num_encoder_blocks=4,
depths=[2, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
hidden_sizes=[16, 32, 64, 128],
downsampling_rates=[1, 4, 8, 16],
num_attention_heads=[1, 2, 4, 8],
is_training=True,
use_labels=True,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
decoder_hidden_size=16,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.sr_ratios = sr_ratios
self.depths = depths
self.hidden_sizes = hidden_sizes
self.downsampling_rates = downsampling_rates
self.num_attention_heads = num_attention_heads
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.decoder_hidden_size = decoder_hidden_size
self.num_labels = num_labels
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return GLPNConfig(
image_size=self.image_size,
num_channels=self.num_channels,
num_encoder_blocks=self.num_encoder_blocks,
depths=self.depths,
hidden_sizes=self.hidden_sizes,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
decoder_hidden_size=self.decoder_hidden_size,
)
def create_and_check_model(self, config, pixel_values, labels):
model = GLPNModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
)
def create_and_check_for_depth_estimation(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = GLPNForDepthEstimation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class GLPNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
pipeline_model_mapping = (
{"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
)
test_head_masking = False
test_pruning = False
test_resize_embeddings = False
def setUp(self):
self.model_tester = GLPNModelTester(self)
self.config_tester = GLPNConfigTester(self, config_class=GLPNConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_depth_estimation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*config_and_inputs)
@unittest.skip("GLPN does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = sum(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
# verify the last attentions (last block, last layer)
expected_seq_len = (self.model_tester.image_size // 32) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:]),
[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_encoder_blocks
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_training(self):
if not self.model_tester.is_training:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in get_values(MODEL_MAPPING):
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py
if model_class.__name__ == "GLPNForDepthEstimation":
batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape
inputs_dict["labels"] = torch.zeros(
[self.model_tester.batch_size, height, width], device=torch_device
).long()
model = model_class(config)
model.to(torch_device)
model.train()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
loss = model(**inputs).loss
loss.backward()
@slow
def test_model_from_pretrained(self):
for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GLPNModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
@slow
class GLPNModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_depth_estimation(self):
image_processor = GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
model = GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device)
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the predicted depth
expected_shape = torch.Size([1, 480, 640])
self.assertEqual(outputs.predicted_depth.shape, expected_shape)
expected_slice = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3], expected_slice, atol=1e-4))
| 14,468 | 39.303621 | 117 | py |
transformers | transformers-main/tests/models/glpn/test_image_processing_glpn.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import GLPNImageProcessor
class GLPNImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size_divisor=32,
do_rescale=True,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size_divisor = size_divisor
self.do_rescale = do_rescale
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = GLPNImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = GLPNImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size_divisor"))
self.assertTrue(hasattr(image_processing, "resample"))
self.assertTrue(hasattr(image_processing, "do_rescale"))
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input (GLPNImageProcessor doesn't support batching)
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0)
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0)
| 4,998 | 38.362205 | 111 | py |
transformers | transformers-main/tests/models/canine/test_tokenization_canine.py | # coding=utf-8
# Copyright 2021 Google AI and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = CanineTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
tokenizer = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def canine_tokenizer(self):
return CanineTokenizer.from_pretrained("google/canine-s")
def get_tokenizer(self, **kwargs) -> CanineTokenizer:
tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
tokenizer._unicode_vocab_size = 1024
return tokenizer
@require_torch
def test_prepare_batch_integration(self):
tokenizer = self.canine_tokenizer
src_text = ["Life is like a box of chocolates.", "You never know what you're gonna get."]
# fmt: off
expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
# fmt: on
batch = tokenizer(src_text, padding=True, return_tensors="pt")
self.assertIsInstance(batch, BatchEncoding)
result = list(batch.input_ids.numpy()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 39), batch.input_ids.shape)
self.assertEqual((2, 39), batch.attention_mask.shape)
@require_torch
def test_encoding_keys(self):
tokenizer = self.canine_tokenizer
src_text = ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
batch = tokenizer(src_text, padding=True, return_tensors="pt")
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertIn("token_type_ids", batch)
@require_torch
def test_max_length_integration(self):
tokenizer = self.canine_tokenizer
tgt_text = [
"What's the weater?",
"It's about 25 degrees.",
]
targets = tokenizer(
text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors="pt"
)
self.assertEqual(32, targets["input_ids"].shape[1])
# cannot use default save_and_load_tokenzier test method because tokenzier has no vocab
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
additional_special_tokens = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
new_additional_special_token = chr(0xE007)
additional_special_tokens.append(new_additional_special_token)
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertIn(new_additional_special_token, after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
def test_add_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, ids = self.get_clean_sequence(tokenizer)
# a special token for Canine can be defined as follows:
SPECIAL_TOKEN = 0xE005
special_token = chr(SPECIAL_TOKEN)
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(len(encoded_special_token), 1)
text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_tokenize_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
SPECIAL_TOKEN_1 = chr(0xE005)
SPECIAL_TOKEN_2 = chr(0xE006)
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True)
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})
token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)
self.assertEqual(len(token_1), 1)
self.assertEqual(len(token_2), 1)
self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
self.assertEqual(token_2[0], SPECIAL_TOKEN_2)
@require_tokenizers
def test_added_token_serializable(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token = chr(NEW_TOKEN)
new_token = AddedToken(new_token, lstrip=True)
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(tmp_dir_name)
tokenizer.from_pretrained(tmp_dir_name)
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token_1 = chr(NEW_TOKEN)
special_tokens_map["additional_special_tokens"] = [new_token_1]
tokenizer_config["additional_special_tokens"] = [new_token_1]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(tmp_dir, extra_ids=0)
self.assertIn(new_token_1, tokenizer_without_change_in_init.additional_special_tokens)
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_1],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_1])
),
)
NEW_TOKEN = 0xE007
new_token_2 = chr(NEW_TOKEN)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = [AddedToken(new_token_2, lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir, additional_special_tokens=new_added_tokens, extra_ids=0
)
self.assertIn(new_token_2, tokenizer.additional_special_tokens)
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_2], tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_2]))
)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input = "hello world"
if self.space_between_special_tokens:
output = "[CLS] hello world [SEP]"
else:
output = input
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
# cannot use default `test_tokenizers_common_ids_setters` method because tokenizer has no vocab
def test_tokenizers_common_ids_setters(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
token_to_test_setters = "a"
token_id_to_test_setters = ord(token_to_test_setters)
for attr in attributes_list:
setattr(tokenizer, attr + "_id", None)
self.assertEqual(getattr(tokenizer, attr), None)
self.assertEqual(getattr(tokenizer, attr + "_id"), None)
setattr(tokenizer, attr + "_id", token_id_to_test_setters)
self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)
setattr(tokenizer, "additional_special_tokens_ids", [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [])
additional_special_token_id = 0xE006
additional_special_token = chr(additional_special_token_id)
setattr(tokenizer, "additional_special_tokens_ids", [additional_special_token_id])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [additional_special_token])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [additional_special_token_id])
# tokenizer has a fixed vocab_size (namely all possible unicode code points)
def test_add_tokens_tokenizer(self):
pass
# CanineTokenizer does not support do_lower_case = True, as each character has its own Unicode code point
# ("b" and "B" for example have different Unicode code points)
def test_added_tokens_do_lower_case(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_np_encode_plus_sent_to_model(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_torch_encode_plus_sent_to_model(self):
pass
# tokenizer can be instantiated without any pretrained files, so no need for pretrained tokenizer list
def test_pretrained_model_lists(self):
pass
# tokenizer does not have vocabulary
def test_get_vocab(self):
pass
# inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
def test_pretokenized_inputs(self):
pass
# tests all ids in vocab => vocab doesn't exist so unnecessary to test
def test_conversion_reversible(self):
pass
| 16,144 | 46.346041 | 208 | py |
transformers | transformers-main/tests/models/canine/test_modeling_canine.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch CANINE model. """
import unittest
from typing import List, Tuple
from transformers import CanineConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, global_rng, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineModel,
)
from transformers.models.canine.modeling_canine import CANINE_PRETRAINED_MODEL_ARCHIVE_LIST
class CanineModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
# let's use a vocab size that's way bigger than BERT's one
# NOTE: this is not a model parameter, just an input
vocab_size=100000,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
num_hash_buckets=16,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.num_hash_buckets = num_hash_buckets
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor(input_ids.shape, self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return CanineConfig(
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
num_hash_buckets=self.num_hash_buckets,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CanineModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = CanineForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = CanineForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = CanineForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = CanineForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class CanineModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
CanineModel,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": CanineModel,
"question-answering": CanineForQuestionAnswering,
"text-classification": CanineForSequenceClassification,
"token-classification": CanineForTokenClassification,
"zero-shot": CanineForSequenceClassification,
}
if is_torch_available()
else {}
)
test_mismatched_shapes = False
test_resize_embeddings = False
test_pruning = False
def setUp(self):
self.model_tester = CanineModelTester(self)
# we set has_text_modality to False as the config has no vocab_size attribute
self.config_tester = ConfigTester(self, config_class=CanineConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
# expected_num_layers equals num_hidden_layers of the deep encoder + 1, + 2 for the first shallow encoder, + 2
# for the final shallow encoder
expected_num_layers = self.model_tester.num_hidden_layers + 1 + 2 + 2
self.assertEqual(len(hidden_states), expected_num_layers)
seq_length = self.model_tester.seq_length
for i in range(expected_num_layers):
if (i < 2) or ((expected_num_layers - i) < 3):
# the expected length of the hidden_states of the first and final shallow encoders
# is equal to the seq_length
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length, self.model_tester.hidden_size],
)
else:
# the expected length of the hidden_states of the deep encoder need to be updated
# for CANINE since the seq length is downsampled
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[seq_length // config.downsampling_rate, self.model_tester.hidden_size],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
seq_len = getattr(self.model_tester, "seq_length", None)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
# we add + 2 due to the 2 shallow encoders
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
if hasattr(self.model_tester, "num_hidden_states_types"):
added_hidden_states = self.model_tester.num_hidden_states_types
else:
added_hidden_states = 1
self.assertEqual(out_len + added_hidden_states, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers + 2)
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads, seq_len, seq_len],
)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
print(model_class)
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
)
def test_headmasking(self):
if not self.test_head_masking:
return
global_rng.seed(42)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
global_rng.seed()
inputs_dict["output_attentions"] = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(
self.model_tester.num_hidden_layers,
self.model_tester.num_attention_heads,
device=torch_device,
)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = self._prepare_for_class(inputs_dict, model_class).copy()
inputs["head_mask"] = head_mask
outputs = model(**inputs, return_dict=True)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
def check_attentions_validity(attentions):
# Remove Nan
for t in attentions:
self.assertLess(
torch.sum(torch.isnan(t)), t.numel() / 4
) # Check we don't have more than 25% nans (arbitrary)
attentions = [
t.masked_fill(torch.isnan(t), 0.0) for t in attentions
] # remove them (the test is less complete)
self.assertAlmostEqual(attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[1][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(attentions[-2][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(attentions[-2][..., -1, :, :].flatten().sum().item(), 0.0)
check_attentions_validity(outputs.attentions)
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_inputs_embeds(self):
# ViT does not use inputs_embeds
pass
@unittest.skip("CANINE does not have a get_input_embeddings() method.")
def test_model_common_attributes(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in CANINE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = CanineModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class CanineModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = CanineModel.from_pretrained("google/canine-s")
# this one corresponds to the first example of the TydiQA dev set (in Swahili)
# fmt: off
input_ids = [57344, 57349, 85, 107, 117, 98, 119, 97, 32, 119, 97, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 110, 105, 32, 107, 105, 97, 115, 105, 32, 103, 97, 110, 105, 63, 57345, 57350, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 44, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 97, 114, 117, 115, 105, 32, 97, 117, 32, 105, 110, 103, 46, 32, 65, 108, 112, 104, 97, 32, 67, 101, 110, 116, 97, 117, 114, 105, 32, 40, 112, 105, 97, 58, 32, 84, 111, 108, 105, 109, 97, 110, 32, 97, 117, 32, 82, 105, 103, 105, 108, 32, 75, 101, 110, 116, 97, 117, 114, 117, 115, 41, 32, 110, 105, 32, 110, 121, 111, 116, 97, 32, 105, 110, 97, 121, 111, 110, 103, 39, 97, 97, 32, 115, 97, 110, 97, 32, 107, 97, 116, 105, 107, 97, 32, 97, 110, 103, 97, 32, 121, 97, 32, 107, 117, 115, 105, 110, 105, 32, 107, 119, 101, 110, 121, 101, 32, 107, 117, 110, 100, 105, 110, 121, 111, 116, 97, 32, 121, 97, 32, 75, 97, 110, 116, 97, 114, 117, 115, 105, 32, 40, 112, 105, 97, 58, 32, 105, 110, 103, 46, 32, 67, 101, 110, 116, 97, 117, 114, 117, 115, 41, 46, 32, 78, 105, 32, 110, 121, 111, 116, 97, 32, 121, 97, 32, 107, 117, 110, 103, 97, 97, 32, 115, 97, 110, 97, 32, 121, 97, 32, 110, 110, 101, 32, 97, 110, 103, 97, 110, 105, 32, 108, 97, 107, 105, 110, 105, 32, 104, 97, 105, 111, 110, 101, 107, 97, 110, 105, 32, 107, 119, 101, 110, 121, 101, 32, 110, 117, 115, 117, 100, 117, 110, 105, 97, 32, 121, 97, 32, 107, 97, 115, 107, 97, 122, 105, 110, 105, 46, 32, 57351, 32, 65, 108, 112, 104, 97, 32, 67, 101, 110, 116, 97, 117, 114, 105, 32, 110, 105, 32, 110, 121, 111, 116, 97, 32, 121, 97, 32, 112, 101, 107, 101, 101, 32, 107, 119, 97, 32, 115, 97, 98, 97, 98, 117, 32, 110, 105, 32, 110, 121, 111, 116, 97, 32, 121, 101, 116, 117, 32, 106, 105, 114, 97, 110, 105, 32, 107, 97, 116, 105, 107, 97, 32, 97, 110, 103, 97, 32, 105, 110, 97, 32, 117, 109, 98, 97, 108, 105, 32, 119, 97, 32, 109, 105, 97, 107, 97, 32, 121, 97, 32, 110, 117, 114, 117, 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0, 0, 0, 0, 0, 0]
attention_mask = [1 if x != 0 else 0 for x in input_ids]
token_type_ids = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
# fmt: on
input_ids = torch.tensor([input_ids])
attention_mask = torch.tensor([attention_mask])
token_type_ids = torch.tensor([token_type_ids])
outputs = model(input_ids, attention_mask, token_type_ids)
# verify sequence output
expected_shape = torch.Size((1, 2048, 768))
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[
[-0.161433131, 0.395568609, 0.0407391489],
[-0.108025983, 0.362060368, -0.544592619],
[-0.141537309, 0.180541009, 0.076907],
]
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-2))
# verify pooled output
expected_shape = torch.Size((1, 768))
self.assertEqual(outputs.pooler_output.shape, expected_shape)
expected_slice = torch.tensor([-0.884311497, -0.529064834, 0.723164916])
self.assertTrue(torch.allclose(outputs.pooler_output[0, :3], expected_slice, atol=1e-2))
| 38,196 | 67.947653 | 8,017 | py |
transformers | transformers-main/tests/models/roformer/test_modeling_flax_roformer.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class FlaxRoFormerModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class FlaxRoFormerModelTest(FlaxModelTesterMixin, unittest.TestCase):
test_head_masking = True
all_model_classes = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def setUp(self):
self.model_tester = FlaxRoFormerModelTester(self)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("junnyu/roformer_chinese_small", from_pt=True)
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
@require_flax
class FlaxRoFormerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
input_ids = jnp.array([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
vocab_size = 50000
expected_shape = (1, 6, vocab_size)
self.assertEqual(output.shape, expected_shape)
expected_slice = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]
)
self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| 5,861 | 34.743902 | 114 | py |
transformers | transformers-main/tests/models/roformer/test_modeling_roformer.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch RoFormer model. """
import unittest
from transformers import RoFormerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
RoFormerForCausalLM,
RoFormerForMaskedLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
RoFormerModel,
)
from transformers.models.roformer.modeling_roformer import (
ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
RoFormerSelfAttention,
RoFormerSinusoidalPositionalEmbedding,
)
class RoFormerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return RoFormerConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RoFormerModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RoFormerModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RoFormerForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_generate_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = RoFormerForCausalLM(config=config).to(torch_device).eval()
torch.manual_seed(0)
output_without_past_cache = model.generate(
input_ids[:1], num_beams=2, max_length=15, do_sample=True, use_cache=False
)
torch.manual_seed(0)
output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=15, do_sample=True)
self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RoFormerForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = RoFormerForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RoFormerForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RoFormerForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = RoFormerForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = RoFormerForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class RoFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
RoFormerModel,
RoFormerForMaskedLM,
RoFormerForCausalLM,
RoFormerForMultipleChoice,
RoFormerForQuestionAnswering,
RoFormerForSequenceClassification,
RoFormerForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (RoFormerForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": RoFormerModel,
"fill-mask": RoFormerForMaskedLM,
"question-answering": RoFormerForQuestionAnswering,
"text-classification": RoFormerForSequenceClassification,
"text-generation": RoFormerForCausalLM,
"token-classification": RoFormerForTokenClassification,
"zero-shot": RoFormerForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = RoFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_generate_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_generate_causal_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
@slow
def test_model_from_pretrained(self):
for model_name in ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = RoFormerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class RoFormerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_masked_lm(self):
model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
with torch.no_grad():
output = model(input_ids)[0]
# TODO Replace vocab size
vocab_size = 50000
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@require_torch
class RoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_basic(self):
input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device)
emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6).to(torch_device)
emb = emb1(input_ids.shape)
desired_weights = torch.tensor(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
).to(torch_device)
self.assertTrue(
torch.allclose(emb, desired_weights, atol=self.tolerance),
msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
)
def test_positional_emb_weights_against_roformer(self):
desired_weights = torch.tensor(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
]
).to(torch_device)
emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512).to(torch_device)
weights = emb1.weight.data[:3, :5].to(torch_device)
self.assertTrue(
torch.allclose(weights, desired_weights, atol=self.tolerance),
msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n",
)
@require_torch
class RoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
tolerance = 1e-4
def test_apply_rotary_position_embeddings(self):
# 2,12,16,64
query_layer = (
torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100
).to(torch_device)
key_layer = (
-torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100
).to(torch_device)
embed_positions = RoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64).to(torch_device)
sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
query_layer, key_layer = RoFormerSelfAttention.apply_rotary_position_embeddings(
sinusoidal_pos, query_layer, key_layer
)
desired_query_layer = torch.tensor(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
]
).to(torch_device)
desired_key_layer = torch.tensor(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
]
).to(torch_device)
self.assertTrue(
torch.allclose(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance),
msg=f"\nexp:\n{desired_query_layer}\ngot:\n{query_layer}\n",
)
self.assertTrue(
torch.allclose(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance),
msg=f"\nexp:\n{desired_key_layer}\ngot:\n{key_layer}\n",
)
| 23,461 | 38.498316 | 117 | py |
transformers | transformers-main/tests/models/plbart/test_tokenization_plbart.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
EN_CODE = 50003
PYTHON_CODE = 50002
@require_sentencepiece
@require_tokenizers
class PLBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = PLBartTokenizer
rust_tokenizer_class = None
test_rust_tokenizer = False
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="base", keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_full_base_tokenizer(self):
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="base", keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
end = tokenizer.vocab_size
language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 4, end)]
self.assertListEqual(language_tokens, ["__java__", "__python__", "__en_XX__", "<mask>"])
code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
input_ids = tokenizer(code).input_ids
self.assertEqual(
tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False),
code,
)
def test_full_multi_tokenizer(self):
tokenizer = PLBartTokenizer(SAMPLE_VOCAB, language_codes="multi", keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
end = tokenizer.vocab_size
language_tokens = [tokenizer.convert_ids_to_tokens(x) for x in range(end - 7, end)]
self.assertListEqual(
language_tokens, ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]
)
code = "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
input_ids = tokenizer(code).input_ids
self.assertEqual(
tokenizer.decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False),
code,
)
@require_torch
@require_sentencepiece
@require_tokenizers
class PLBartPythonEnIntegrationTest(unittest.TestCase):
checkpoint_name = "uclanlp/plbart-python-en_XX"
src_text = [
"def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])",
"def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])",
]
tgt_text = [
"Returns the maximum value of a b c.",
"Sums the values of a b c.",
]
expected_src_tokens = [
134,
5452,
33460,
33441,
33463,
33465,
33463,
33449,
988,
20,
33456,
19,
33456,
771,
39,
4258,
889,
3318,
33441,
33463,
33465,
33463,
33449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def setUpClass(cls):
cls.tokenizer: PLBartTokenizer = PLBartTokenizer.from_pretrained(
cls.checkpoint_name, language_codes="base", src_lang="python", tgt_lang="en_XX"
)
cls.pad_token_id = 1
return cls
def check_language_codes(self):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"], 50001)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"], 50002)
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"], 50003)
def test_python_en_tokenizer_batch_encode_plus(self):
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
self.assertListEqual(self.expected_src_tokens, ids)
def test_python_en_tokenizer_decode_ignores_language_codes(self):
self.assertIn(PYTHON_CODE, self.tokenizer.all_special_ids)
generated_ids = [EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
expected_english = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
self.assertEqual(result, expected_english)
self.assertNotIn(self.tokenizer.eos_token, result)
def test_python_en_tokenizer_truncation(self):
src_text = ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
self.assertIsInstance(src_text[0], str)
desired_max_length = 10
ids = self.tokenizer(src_text, max_length=desired_max_length, truncation=True).input_ids[0]
self.assertEqual(ids[-2], 2)
self.assertEqual(ids[-1], PYTHON_CODE)
self.assertEqual(len(ids), desired_max_length)
def test_mask_token(self):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]), [50004, 50001])
def test_special_tokens_unaffacted_by_save_load(self):
tmpdirname = tempfile.mkdtemp()
original_special_tokens = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(tmpdirname)
new_tok = PLBartTokenizer.from_pretrained(tmpdirname)
self.assertDictEqual(new_tok.fairseq_tokens_to_ids, original_special_tokens)
@require_torch
def test_batch_fairseq_parity(self):
batch = self.tokenizer(self.src_text, text_target=self.tgt_text, padding=True, return_tensors="pt")
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist(), [2, PYTHON_CODE])
self.assertEqual(batch.decoder_input_ids[1][0], EN_CODE)
self.assertEqual(batch.decoder_input_ids[1][-1], 2)
self.assertEqual(batch.labels[1][-2:].tolist(), [2, EN_CODE])
@require_torch
def test_python_en_tokenizer_prepare_batch(self):
batch = self.tokenizer(
self.src_text,
text_target=self.tgt_text,
padding=True,
truncation=True,
max_length=len(self.expected_src_tokens),
return_tensors="pt",
)
batch["decoder_input_ids"] = shift_tokens_right(batch["labels"], self.tokenizer.pad_token_id)
self.assertIsInstance(batch, BatchEncoding)
self.assertEqual((2, 26), batch.input_ids.shape)
self.assertEqual((2, 26), batch.attention_mask.shape)
result = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens, result)
self.assertEqual(2, batch.decoder_input_ids[0, -1]) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens, [])
self.assertEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id, PYTHON_CODE])
def test_seq2seq_max_length(self):
batch = self.tokenizer(self.src_text, padding=True, truncation=True, max_length=3, return_tensors="pt")
targets = self.tokenizer(
text_target=self.tgt_text, padding=True, truncation=True, max_length=10, return_tensors="pt"
)
labels = targets["input_ids"]
batch["decoder_input_ids"] = shift_tokens_right(labels, self.tokenizer.pad_token_id)
self.assertEqual(batch.input_ids.shape[1], 3)
self.assertEqual(batch.decoder_input_ids.shape[1], 10)
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs(
"A test", return_tensors="pt", src_lang="en_XX", tgt_lang="java"
)
self.assertEqual(
nested_simplify(inputs),
{
# A, test, EOS, en_XX
"input_ids": [[150, 242, 2, 50003]],
"attention_mask": [[1, 1, 1, 1]],
# java
"forced_bos_token_id": 50001,
},
)
| 13,068 | 33.757979 | 119 | py |
transformers | transformers-main/tests/models/plbart/test_modeling_plbart.py | # coding=utf-8
# Copyright 2022, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch PLBART model. """
import copy
import tempfile
import unittest
from transformers import PLBartConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
AutoTokenizer,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
)
from transformers.models.plbart.modeling_plbart import PLBartDecoder, PLBartEncoder
def prepare_plbart_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class PLBartModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=100,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = input_ids.clamp(3)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_plbart_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_config(self):
return PLBartConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = PLBartModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_with_past_key_values = model(
next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values
)
output_from_past = output_with_past_key_values["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = PLBartModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = PLBartEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = PLBartDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class PLBartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(PLBartModel, PLBartForConditionalGeneration, PLBartForSequenceClassification) if is_torch_available() else ()
)
all_generative_model_classes = (PLBartForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": PLBartForConditionalGeneration,
"feature-extraction": PLBartModel,
"summarization": PLBartForConditionalGeneration,
"text-classification": PLBartForSequenceClassification,
"text-generation": PLBartForCausalLM,
"text2text-generation": PLBartForConditionalGeneration,
"translation": PLBartForConditionalGeneration,
"zero-shot": PLBartForSequenceClassification,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = False # Fix me Michael
test_pruning = False
test_missing_keys = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `PLBartConfig` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def setUp(self):
self.model_tester = PLBartModelTester(self)
self.config_tester = ConfigTester(self, config_class=PLBartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
# PLBartForSequenceClassification does not support inputs_embeds
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (PLBartModel, PLBartForConditionalGeneration):
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = PLBartForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
@require_torch
@require_sentencepiece
@require_tokenizers
class AbstractSeq2SeqIntegrationTest(unittest.TestCase):
maxDiff = 1000 # longer string compare tracebacks
checkpoint_name = None
@classmethod
def setUpClass(cls):
cls.tokenizer = AutoTokenizer.from_pretrained(cls.checkpoint_name, use_fast=False)
return cls
@cached_property
def model(self):
"""Only load the model if needed."""
model = PLBartForConditionalGeneration.from_pretrained(self.checkpoint_name).to(torch_device)
if "cuda" in torch_device:
model = model.half()
return model
@require_torch
@require_sentencepiece
@require_tokenizers
class PLBartJavaCsIntegrationTest(AbstractSeq2SeqIntegrationTest):
checkpoint_name = "uclanlp/plbart-java-cs"
src_text = [
"public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}",
"public int product(int a, int b, int c){return a*b*c;}",
]
tgt_text = [
"public int maximum(int a, int b, int c){return Math.Max(",
"public int Product(int a, int b, int c){return a * b *",
]
@slow
def test_java_cs_generate_one(self):
batch = self.tokenizer(
["public int maximum(int a, int b, int c){return Math.max(a, Math.max(b, c));}"], return_tensors="pt"
)
batch = batch.to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
self.assertEqual(self.tgt_text[0], decoded[0])
# self.assertEqual(self.tgt_text[1], decoded[1])
@slow
def test_java_cs_generate_batch(self):
batch = self.tokenizer(self.src_text, return_tensors="pt", padding=True, truncation=True)
batch = batch.to(torch_device)
translated_tokens = self.model.generate(**batch)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert self.tgt_text == decoded
def test_plbart_java_cs_config(self):
plbart_models = ["uclanlp/plbart-java-cs"]
expected = {"scale_embedding": True}
for name in plbart_models:
config = PLBartConfig.from_pretrained(name)
for k, v in expected.items():
try:
self.assertEqual(v, getattr(config, k))
except AssertionError as e:
e.args += (name, k)
raise
def test_plbart_fast_forward(self):
config = PLBartConfig(
vocab_size=99,
d_model=24,
encoder_layers=2,
decoder_layers=2,
encoder_attention_heads=2,
decoder_attention_heads=2,
encoder_ffn_dim=32,
decoder_ffn_dim=32,
max_position_embeddings=48,
add_final_layer_norm=True,
)
lm_model = PLBartForConditionalGeneration(config).to(torch_device)
context = torch.tensor(
[[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
)
summary = torch.tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], device=torch_device, dtype=torch.long)
result = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(result.logits.shape, expected_shape)
@require_torch
@require_sentencepiece
@require_tokenizers
class PLBartBaseIntegrationTest(AbstractSeq2SeqIntegrationTest):
checkpoint_name = "uclanlp/plbart-base"
src_text = ["Is 0 the first Fibonacci number ?", "Find the sum of all prime numbers ."]
tgt_text = ["0 the first Fibonacci number?", "the sum of all prime numbers.......... the the"]
def test_base_generate(self):
inputs = self.tokenizer([self.src_text[0]], return_tensors="pt").to(torch_device)
src_lan = self.tokenizer._convert_lang_code_special_format("en_XX")
translated_tokens = self.model.generate(
input_ids=inputs["input_ids"].to(torch_device),
decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan],
)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
self.assertEqual(self.tgt_text[0], decoded[0])
@slow
def test_fill_mask(self):
inputs = self.tokenizer(["Is 0 the <mask> Fibonacci <mask> ?"], return_tensors="pt").to(torch_device)
src_lan = self.tokenizer._convert_lang_code_special_format("en_XX")
outputs = self.model.generate(
inputs["input_ids"], decoder_start_token_id=self.tokenizer.lang_code_to_id[src_lan], num_beams=1
)
prediction: str = self.tokenizer.batch_decode(
outputs, clean_up_tokenization_spaces=True, skip_special_tokens=True
)[0]
self.assertEqual(prediction, "0 0 the 0 the 0 the 0 the 0 the 0 the 0 the 0 the")
class PLBartStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = PLBartConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (config, input_ids, attention_mask, lm_labels)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = PLBartDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = PLBartDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, attention_mask, lm_labels) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class PLBartStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (PLBartDecoder, PLBartForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (PLBartForCausalLM,) if is_torch_available() else ()
test_pruning = False
is_encoder_decoder = False
def setUp(self):
self.model_tester = PLBartStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=PLBartConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
| 27,102 | 39.756391 | 123 | py |
transformers | transformers-main/tests/models/xlm_prophetnet/test_modeling_xlm_prophetnet.py | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team, The Microsoft Research team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer
@require_torch
class XLMProphetNetModelIntegrationTest(unittest.TestCase):
@slow
def test_pretrained_checkpoint_hidden_states(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained("microsoft/xprophetnet-large-wiki100-cased")
model.to(torch_device)
# encoder-decoder outputs
encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
decoder_prev_ids = torch.tensor(
[[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]
).to(torch_device)
output = model(
input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids
)
output_predited_logis = output[0]
expected_shape = torch.Size((1, 14, 250012))
self.assertEqual(output_predited_logis.shape, expected_shape)
expected_slice = torch.tensor(
[[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]
).to(torch_device)
self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3], expected_slice, atol=1e-4))
# encoder outputs
encoder_outputs = model.prophetnet.encoder(encoder_ids)[0]
expected_encoder_outputs_slice = torch.tensor(
[[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]
).to(torch_device)
expected_shape_encoder = torch.Size((1, 4, 1024))
self.assertEqual(encoder_outputs.shape, expected_shape_encoder)
self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3], expected_encoder_outputs_slice, atol=1e-4))
# decoder outputs
decoder_outputs = model.prophetnet.decoder(
decoder_prev_ids,
encoder_hidden_states=encoder_outputs,
)
predicting_streams = decoder_outputs[1].view(1, model.config.ngram, 14, -1)
predicting_streams_logits = model.lm_head(predicting_streams)
next_first_stream_logits = predicting_streams_logits[:, 0]
self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_ntg_hidden_states(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"
)
model.to(torch_device)
encoder_ids = torch.tensor([[17, 96208, 103471, 2]]).to(torch_device)
decoder_prev_ids = torch.tensor(
[[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]
).to(torch_device)
output = model(
input_ids=encoder_ids, attention_mask=None, encoder_outputs=None, decoder_input_ids=decoder_prev_ids
)
output_predited_logis = output[0]
expected_shape = torch.Size((1, 14, 250012))
self.assertEqual(output_predited_logis.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.tensor(
[[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]
).to(torch_device)
self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_xprophetnet_ntg_inference(self):
model = XLMProphetNetForConditionalGeneration.from_pretrained(
"microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"
)
model.to(torch_device)
model.config.max_length = 512
tokenizer = XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased-xglue-ntg")
EN_SENTENCE = (
"Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
" January 14, 2020, according to the official portal of the organization. From that day, users of this"
" system will not be able to receive security updates, which could make their computers vulnerable to"
" cyber attacks."
)
RU_SENTENCE = (
"орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
" после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
" этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
" к кибератакам."
)
ZH_SENTENCE = (
"根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
)
input_ids = tokenizer(
[EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE], padding=True, max_length=255, return_tensors="pt"
).input_ids
input_ids = input_ids.to(torch_device)
summary_ids = model.generate(
input_ids, num_beams=10, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
generated_titles = [tokenizer.decode(g, skip_special_tokens=True) for g in summary_ids]
EXPECTED_TITLE_EN = "Microsoft to end Windows 7 free support after January 14, 2020"
EXPECTED_TITLE_RU = "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
EXPECTED_TITLE_ZH = "微软打算终止对Windows 7操作系统的免费支持"
self.assertListEqual(
[EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH],
generated_titles,
)
summary_ids_beam1 = model.generate(
input_ids, num_beams=1, length_penalty=1.0, no_repeat_ngram_size=3, early_stopping=True
)
generated_titles_beam1_tok = [
tokenizer.convert_ids_to_tokens(g, skip_special_tokens=True) for g in summary_ids_beam1
]
EXPECTED_TITLE_EN_BEAM1_TOK = "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(" ")
EXPECTED_TITLE_RU_BEAM1_TOK = "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
" "
)
EXPECTED_TITLE_ZH_BEAM1_TOK = "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(" ")
self.assertListEqual(
[EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK],
generated_titles_beam1_tok,
)
| 7,226 | 46.235294 | 149 | py |
transformers | transformers-main/tests/models/megatron_gpt2/test_modeling_megatron_gpt2.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
import torch
from transformers import GPT2LMHeadModel
@require_torch
@require_sentencepiece
@require_tokenizers
class MegatronGPT2IntegrationTest(unittest.TestCase):
@slow
@unittest.skip("Model is not available.")
def test_inference_no_head(self):
directory = "nvidia/megatron-gpt2-345m/"
if "MYDIR" in os.environ:
directory = os.path.join(os.environ["MYDIR"], directory)
model = GPT2LMHeadModel.from_pretrained(directory)
model.to(torch_device)
model.half()
input_ids = torch.tensor(
[[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]],
device=torch_device,
dtype=torch.long,
)
with torch.no_grad():
output = model(input_ids).logits
expected_shape = torch.Size((1, 9, 50257))
self.assertEqual(output.shape, expected_shape)
expected_diag = torch.tensor(
[
4.9414,
-0.2920,
-1.2148,
-4.0273,
-0.5161,
-5.2109,
-1.2412,
-1.8301,
-1.7734,
-4.7148,
-0.2317,
-1.0811,
-2.1777,
0.4141,
-3.7969,
-4.0586,
-2.5332,
-3.3809,
4.3867,
],
device=torch_device,
dtype=torch.half,
)
for i in range(19):
r, c = 8 * i // 17, 2792 * i # along the diagonal
computed, expected = output[0, r, c], expected_diag[i]
msg = f"row={r} col={c} computed={computed} expected={expected}"
self.assertAlmostEqual(computed, expected, delta=1e-4, msg=msg)
| 2,656 | 29.895349 | 115 | py |
transformers | transformers-main/tests/models/decision_transformer/test_modeling_decision_transformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch DecisionTransformer model. """
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import DecisionTransformerModel
from transformers.models.decision_transformer.modeling_decision_transformer import (
DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
class DecisionTransformerModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
act_dim=6,
state_dim=17,
hidden_size=23,
max_length=11,
is_training=True,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.act_dim = act_dim
self.state_dim = state_dim
self.hidden_size = hidden_size
self.max_length = max_length
self.is_training = is_training
def prepare_config_and_inputs(self):
states = floats_tensor((self.batch_size, self.seq_length, self.state_dim))
actions = floats_tensor((self.batch_size, self.seq_length, self.act_dim))
rewards = floats_tensor((self.batch_size, self.seq_length, 1))
returns_to_go = floats_tensor((self.batch_size, self.seq_length, 1))
timesteps = ids_tensor((self.batch_size, self.seq_length), vocab_size=1000)
attention_mask = random_attention_mask((self.batch_size, self.seq_length))
config = self.get_config()
return (
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
)
def get_config(self):
return DecisionTransformerConfig(
batch_size=self.batch_size,
seq_length=self.seq_length,
act_dim=self.act_dim,
state_dim=self.state_dim,
hidden_size=self.hidden_size,
max_length=self.max_length,
)
def create_and_check_model(
self,
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
):
model = DecisionTransformerModel(config=config)
model.to(torch_device)
model.eval()
result = model(states, actions, rewards, returns_to_go, timesteps, attention_mask)
self.parent.assertEqual(result.state_preds.shape, states.shape)
self.parent.assertEqual(result.action_preds.shape, actions.shape)
self.parent.assertEqual(result.return_preds.shape, returns_to_go.shape)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.seq_length * 3, self.hidden_size)
) # seq length *3 as there are 3 modelities: states, returns and actions
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
states,
actions,
rewards,
returns_to_go,
timesteps,
attention_mask,
) = config_and_inputs
inputs_dict = {
"states": states,
"actions": actions,
"rewards": rewards,
"returns_to_go": returns_to_go,
"timesteps": timesteps,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class DecisionTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (DecisionTransformerModel,) if is_torch_available() else ()
all_generative_model_classes = ()
pipeline_model_mapping = {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}
# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
test_generate_without_input_ids = False
# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_attention_outputs = False
test_hidden_states_output = False
test_inputs_embeds = False
test_model_common_attributes = False
test_gradient_checkpointing = False
test_torchscript = False
def setUp(self):
self.model_tester = DecisionTransformerModelTester(self)
self.config_tester = ConfigTester(self, config_class=DecisionTransformerConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DecisionTransformerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = [
"states",
"actions",
"rewards",
"returns_to_go",
"timesteps",
"attention_mask",
]
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
@require_torch
class DecisionTransformerModelIntegrationTest(unittest.TestCase):
@slow
def test_autoregressive_prediction(self):
"""
An integration test that performs autoregressive prediction of state, action and return
from a sequence of state, actions and returns. Test is performed over two timesteps.
"""
NUM_STEPS = 2 # number of steps of autoregressive prediction we will perform
TARGET_RETURN = 10 # defined by the RL environment, may be normalized
model = DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert")
model = model.to(torch_device)
config = model.config
torch.manual_seed(0)
state = torch.randn(1, 1, config.state_dim).to(device=torch_device, dtype=torch.float32) # env.reset()
expected_outputs = torch.tensor(
[[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]], device=torch_device
)
returns_to_go = torch.tensor(TARGET_RETURN, device=torch_device, dtype=torch.float32).reshape(1, 1, 1)
states = state
actions = torch.zeros(1, 0, config.act_dim, device=torch_device, dtype=torch.float32)
rewards = torch.zeros(1, 0, device=torch_device, dtype=torch.float32)
timesteps = torch.tensor(0, device=torch_device, dtype=torch.long).reshape(1, 1)
for step in range(NUM_STEPS):
actions = torch.cat([actions, torch.zeros(1, 1, config.act_dim, device=torch_device)], dim=1)
rewards = torch.cat([rewards, torch.zeros(1, 1, device=torch_device)], dim=1)
attention_mask = torch.ones(1, states.shape[1]).to(dtype=torch.long, device=states.device)
with torch.no_grad():
_, action_pred, _ = model(
states=states,
actions=actions,
rewards=rewards,
returns_to_go=returns_to_go,
timesteps=timesteps,
attention_mask=attention_mask,
return_dict=False,
)
self.assertEqual(action_pred.shape, actions.shape)
self.assertTrue(torch.allclose(action_pred[0, -1], expected_outputs[step], atol=1e-4))
state, reward, _, _ = ( # env.step(action)
torch.randn(1, 1, config.state_dim).to(device=torch_device, dtype=torch.float32),
1.0,
False,
{},
)
actions[-1] = action_pred[0, -1]
states = torch.cat([states, state], dim=1)
pred_return = returns_to_go[0, -1] - reward
returns_to_go = torch.cat([returns_to_go, pred_return.reshape(1, 1, 1)], dim=1)
timesteps = torch.cat(
[timesteps, torch.ones((1, 1), device=torch_device, dtype=torch.long) * (step + 1)], dim=1
)
| 9,675 | 37.245059 | 116 | py |
transformers | transformers-main/tests/models/git/test_modeling_git.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import GitConfig, GitProcessor, GitVisionConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, GitForCausalLM, GitModel, GitVisionModel
from transformers.models.git.modeling_git import GIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class GitVisionModelTester:
def __init__(
self,
parent,
batch_size=12,
image_size=32,
patch_size=16,
num_channels=3,
is_training=True,
hidden_size=32,
projection_dim=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
dropout=0.1,
attention_dropout=0.1,
initializer_range=0.02,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.hidden_size = hidden_size
self.projection_dim = projection_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.scope = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
num_patches = (image_size // patch_size) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def get_config(self):
return GitVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values):
model = GitVisionModel(config=config)
model.to(torch_device)
model.eval()
with torch.no_grad():
result = model(pixel_values)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
image_size = (self.image_size, self.image_size)
patch_size = (self.patch_size, self.patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class GitVisionModelTest(ModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as GIT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (GitVisionModel,) if is_torch_available() else ()
fx_compatible = True
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = GitVisionModelTester(self)
self.config_tester = ConfigTester(self, config_class=GitVisionConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="GIT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_training(self):
pass
def test_training_gradient_checkpointing(self):
pass
@unittest.skip(reason="GitVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_from_base(self):
pass
@unittest.skip(reason="GitVisionModel has no base class and is not available in MODEL_MAPPING")
def test_save_load_fast_init_to_base(self):
pass
@slow
def test_model_from_pretrained(self):
for model_name in GIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GitVisionModel.from_pretrained(model_name)
self.assertIsNotNone(model)
class GitModelTester:
def __init__(
self,
parent,
num_channels=3,
image_size=32,
patch_size=16,
batch_size=13,
text_seq_length=7,
is_training=True,
use_input_mask=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.num_channels = num_channels
self.image_size = image_size
self.patch_size = patch_size
self.batch_size = batch_size
self.text_seq_length = text_seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
# make sure the BOS, EOS and PAD tokens are within the vocab
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
# for GIT, the sequence length is the sum of the text and patch tokens, + 1 due to the CLS token
self.seq_length = self.text_seq_length + int((self.image_size / self.patch_size) ** 2) + 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.text_seq_length])
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, input_ids, input_mask, pixel_values
def get_config(self):
"""
Returns a tiny configuration by default.
"""
return GitConfig(
vision_config={
"num_channels": self.num_channels,
"image_size": self.image_size,
"patch_size": self.patch_size,
},
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
def create_and_check_model(self, config, input_ids, input_mask, pixel_values):
model = GitModel(config=config)
model.to(torch_device)
model.eval()
# inference with pixel values
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# inference without pixel values
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
)
def create_and_check_for_causal_lm(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# inference with pixel values
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
# inference without pixel values
result = model(input_ids, attention_mask=input_mask)
result = model(input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.vocab_size))
# training
result = model(input_ids, attention_mask=input_mask, pixel_values=pixel_values, labels=input_ids)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertTrue(result.loss.item() > 0)
def _test_beam_search_generate(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# generate
generated_ids = model.generate(
input_ids,
attention_mask=input_mask,
pixel_values=pixel_values,
do_sample=False,
max_length=20,
num_beams=2,
num_return_sequences=2,
)
self.parent.assertEqual(generated_ids.shape, (self.batch_size * 2, 20))
def _test_batched_generate_captioning(self, config, input_ids, input_mask, pixel_values):
model = GitForCausalLM(config=config)
model.to(torch_device)
model.eval()
# generate
generated_ids = model.generate(
input_ids=None, # captioning -> no input_ids
attention_mask=None,
pixel_values=pixel_values,
do_sample=False,
max_length=20,
num_beams=2,
num_return_sequences=2,
)
self.parent.assertEqual(generated_ids.shape, (self.batch_size * 2, 20))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
pixel_values,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": input_mask,
"pixel_values": pixel_values,
}
return config, inputs_dict
@require_torch
class GitModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (GitModel, GitForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (GitForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": GitModel, "image-to-text": GitForCausalLM, "text-generation": GitForCausalLM}
if is_torch_available()
else {}
)
fx_compatible = False
test_torchscript = False
# special case for GitForCausalLM model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_CAUSAL_LM_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length),
dtype=torch.long,
device=torch_device,
)
return inputs_dict
def setUp(self):
self.model_tester = GitModelTester(self)
self.config_tester = ConfigTester(self, config_class=GitConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_beam_search_generate(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester._test_beam_search_generate(*config_and_inputs)
def test_batched_generate_captioning(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester._test_batched_generate_captioning(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in GIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GitModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@unittest.skip(reason="GIT has pixel values as additional input")
def test_beam_search_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_contrastive_generate(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_contrastive_generate_dict_outputs_use_cache(self):
pass
@unittest.skip(reason="GIT has pixel values as additional input")
def test_greedy_generate_dict_outputs_use_cache(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
@require_torch
@require_vision
@slow
class GitModelIntegrationTest(unittest.TestCase):
def test_forward_pass(self):
processor = GitProcessor.from_pretrained("microsoft/git-base")
model = GitForCausalLM.from_pretrained("microsoft/git-base")
model.to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, text="hello world", return_tensors="pt").to(torch_device)
with torch.no_grad():
outputs = model(**inputs)
expected_shape = torch.Size((1, 201, 30522))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.9514, -0.9512, -0.9507], [-0.5454, -0.5453, -0.5453], [-0.8862, -0.8857, -0.8848]],
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice, atol=1e-4))
def test_inference_image_captioning(self):
processor = GitProcessor.from_pretrained("microsoft/git-base")
model = GitForCausalLM.from_pretrained("microsoft/git-base")
model.to(torch_device)
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
outputs = model.generate(
pixel_values=pixel_values, max_length=20, output_scores=True, return_dict_in_generate=True
)
generated_caption = processor.batch_decode(outputs.sequences, skip_special_tokens=True)[0]
expected_shape = torch.Size((1, 9))
self.assertEqual(outputs.sequences.shape, expected_shape)
self.assertEquals(generated_caption, "two cats laying on a pink blanket")
self.assertTrue(outputs.scores[-1].shape, expected_shape)
expected_slice = torch.tensor([[-0.8805, -0.8803, -0.8799]], device=torch_device)
self.assertTrue(torch.allclose(outputs.scores[-1][0, :3], expected_slice, atol=1e-4))
def test_visual_question_answering(self):
processor = GitProcessor.from_pretrained("microsoft/git-base-textvqa")
model = GitForCausalLM.from_pretrained("microsoft/git-base-textvqa")
model.to(torch_device)
# prepare image
file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset")
image = Image.open(file_path).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# prepare question
question = "what does the front of the bus say at the top?"
input_ids = processor(text=question, add_special_tokens=False).input_ids
input_ids = [processor.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0).to(torch_device)
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=20)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
expected_shape = torch.Size((1, 15))
self.assertEqual(generated_ids.shape, expected_shape)
self.assertEquals(generated_caption, "what does the front of the bus say at the top? special")
def test_batched_generation(self):
processor = GitProcessor.from_pretrained("microsoft/git-base-coco")
model = GitForCausalLM.from_pretrained("microsoft/git-base-coco")
model.to(torch_device)
# create batch of size 2
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(images=[image, image], return_tensors="pt")
pixel_values = inputs.pixel_values.to(torch_device)
# we have to prepare `input_ids` with the same batch size as `pixel_values`
start_token_id = model.config.bos_token_id
input_ids = torch.tensor([[start_token_id], [start_token_id]], device=torch_device)
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
generated_captions = processor.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEquals(generated_captions, ["two cats sleeping on a pink blanket next to remotes."] * 2)
| 21,681 | 38.565693 | 118 | py |
transformers | transformers-main/tests/models/git/test_processor_git.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
@require_vision
class GitProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
image_processor = CLIPImageProcessor()
tokenizer = BertTokenizer.from_pretrained(
"hf-internal-testing/tiny-random-BertModel", model_input_names=["input_ids", "attention_mask"]
)
processor = GitProcessor(image_processor, tokenizer)
processor.save_pretrained(self.tmpdirname)
def get_tokenizer(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
def get_image_processor(self, **kwargs):
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def prepare_image_inputs(self):
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
or a list of PyTorch tensors if one specifies torchify=True.
"""
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
return image_inputs
def test_save_load_pretrained_additional_features(self):
processor = GitProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = GitProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor, CLIPImageProcessor)
def test_image_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
image_input = self.prepare_image_inputs()
input_feat_extract = image_processor(image_input, return_tensors="np")
input_processor = processor(images=image_input, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_tokenizer(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
encoded_processor = processor(text=input_str)
encoded_tok = tokenizer(input_str, return_token_type_ids=False)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key])
def test_processor(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_tokenizer_decode(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
decoded_processor = processor.batch_decode(predicted_ids)
decoded_tok = tokenizer.batch_decode(predicted_ids)
self.assertListEqual(decoded_tok, decoded_processor)
def test_model_input_names(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = GitProcessor(tokenizer=tokenizer, image_processor=image_processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(text=input_str, images=image_input)
# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
| 5,806 | 36.707792 | 116 | py |
transformers | transformers-main/tests/models/jukebox/test_tokenization_jukebox.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class JukeboxTokenizationTest(unittest.TestCase):
tokenizer_class = JukeboxTokenizer
metas = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": """I met a traveller from an antique land,
Who said "Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
@require_torch
def test_1b_lyrics_tokenizer(self):
"""
how to run the same test with openAI
...
"""
import torch
tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
tokens = tokenizer(**self.metas)["input_ids"]
# fmt: off
EXPECTED_OUTPUT = [
torch.tensor([[
0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
76, 76]]),
torch.tensor([[0, 0, 0, 1069, 11]]),
torch.tensor([[0, 0, 0, 1069, 11]]),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))
@require_torch
def test_5b_lyrics_tokenizer(self):
"""
The outputs are similar that open AI but do not have the same format as this one is adapted to the HF integration.
"""
import torch
tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
tokens = tokenizer(**self.metas)["input_ids"]
# fmt: off
EXPECTED_OUTPUT = [
torch.tensor([[
0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
77, 77, 77, 77, 77, 77]]),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
]
# fmt: on
self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0]))
self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1]))
self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))
| 11,196 | 52.319048 | 122 | py |
transformers | transformers-main/tests/models/jukebox/test_modeling_jukebox.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest import skip
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow
from transformers.trainer_utils import set_seed
if is_torch_available():
import torch
from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer
@require_torch
class Jukebox1bModelTester(unittest.TestCase):
all_model_classes = (JukeboxModel,) if is_torch_available() else ()
model_id = "openai/jukebox-1b-lyrics"
metas = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": """I met a traveller from an antique land,
Who said "Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
# fmt: off
EXPECTED_OUTPUT_2 = [
1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534,
1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445,
333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883,
1405, 1276, 1455, 1228
]
EXPECTED_OUTPUT_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653
]
EXPECTED_OUTPUT_1 = [
1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416,
844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125,
191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872,
1262, 869, 1728, 747
]
EXPECTED_OUTPUT_1_PT_2 = [
416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416
]
EXPECTED_OUTPUT_0 = [
1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442,
616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902,
293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008,
1186, 1015, 1777, 268
]
EXPECTED_OUTPUT_0_PT_2 = [
854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842,
185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842,
591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842,
185, 842, 591, 89
]
EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76]
EXPECTED_PRIMED_0 = [
390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
1907, 1788, 596, 1626
]
EXPECTED_PRIMED_1 = [
1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824,
1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599,
1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895,
1063, 1433, 1433, 1599
]
EXPECTED_PRIMED_2 = [
1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927,
96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331,
1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619,
197, 391, 302, 1930
]
EXPECTED_VQVAE_ENCODE = [
390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002,
1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002,
1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907,
1907, 1788, 596, 1626
]
EXPECTED_VQVAE_DECODE = [
-0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664,
-0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025,
0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614,
0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336,
0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531
]
EXPECTED_AUDIO_COND = [
0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440,
0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617,
-0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970,
-0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082
]
EXPECTED_META_COND = [
0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068,
0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743,
-0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536,
-0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456
]
EXPECTED_LYRIC_COND = [
76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33,
45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76
]
# fmt: on
def prepare_inputs(self):
tokenizer = JukeboxTokenizer.from_pretrained(self.model_id)
tokens = tokenizer(**self.metas)["input_ids"]
return tokens
@slow
def test_sampling(self):
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
labels = self.prepare_inputs()
set_seed(0)
zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False)
self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])
set_seed(0)
zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False)
self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])
set_seed(0)
zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False)
self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])
@slow
def test_conditioning(self):
torch.backends.cuda.matmul.allow_tf32 = False
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
labels = self.prepare_inputs()
set_seed(0)
zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)]
top_prior = model.priors[0]
start = 0
music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx)
metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0)
self.assertIsNone(music_token_conds)
self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND)
audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata)
torch.testing.assert_allclose(
audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4
)
torch.testing.assert_allclose(
metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4
)
torch.testing.assert_allclose(
lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4
)
@slow
def test_primed_sampling(self):
torch.backends.cuda.matmul.allow_tf32 = False
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
set_seed(0)
waveform = torch.rand((1, 5120, 1))
tokens = list(self.prepare_inputs())
zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None]
zs = model._sample(
zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens
)
torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0))
upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long()
zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None]
zs = model._sample(
zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens
)
torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1))
upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long()
zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]]
zs = model._sample(
zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens
)
torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2))
@slow
def test_vqvae(self):
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
set_seed(0)
x = torch.rand((1, 5120, 1))
with torch.no_grad():
zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0])
torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE))
with torch.no_grad():
x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0])
torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4)
@require_torch
class Jukebox5bModelTester(unittest.TestCase):
all_model_classes = (JukeboxModel,) if is_torch_available() else ()
model_id = "openai/jukebox-5b-lyrics"
metas = {
"artist": "Zac Brown Band",
"genres": "Country",
"lyrics": """I met a traveller from an antique land,
Who said "Two vast and trunkless legs of stone
Stand in the desert. . . . Near them, on the sand,
Half sunk a shattered visage lies, whose frown,
And wrinkled lip, and sneer of cold command,
Tell that its sculptor well those passions read
Which yet survive, stamped on these lifeless things,
The hand that mocked them, and the heart that fed;
And on the pedestal, these words appear:
My name is Ozymandias, King of Kings;
Look on my Works, ye Mighty, and despair!
Nothing beside remains. Round the decay
Of that colossal Wreck, boundless and bare
The lone and level sands stretch far away
""",
}
# fmt: off
EXPECTED_OUTPUT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272
]
EXPECTED_OUTPUT_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
]
EXPECTED_OUTPUT_1 = [
1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
]
EXPECTED_OUTPUT_1_PT_2 = [
416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416
]
EXPECTED_OUTPUT_0 = [
1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616,
616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290,
290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755,
234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290,
1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616
]
EXPECTED_OUTPUT_0_PT_2 = [
854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185,
417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842,
1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591,
89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842,
185, 89, 591, 842, 185, 842, 591, 632
]
EXPECTED_GPU_OUTPUTS_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653
]
EXPECTED_GPU_OUTPUTS_2_PT_2 = [
1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653,
653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228,
710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475,
1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864,
252, 1229, 1434, 2001
]
EXPECTED_GPU_OUTPUTS_1 = [
1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416,
416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416
]
EXPECTED_GPU_OUTPUTS_0 = [
491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613,
844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613,
185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979,
307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842,
307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89
]
# fmt: on
def prepare_inputs(self, model_id):
tokenizer = JukeboxTokenizer.from_pretrained(model_id)
tokens = tokenizer(**self.metas)["input_ids"]
return tokens
@slow
def test_sampling(self):
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
labels = self.prepare_inputs(self.model_id)
set_seed(0)
zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)]
zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2])
set_seed(0)
zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2])
set_seed(0)
zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2])
@slow
@skip("Not enough GPU memory on CI runners")
def test_slow_sampling(self):
model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval()
labels = [i.cuda() for i in self.prepare_inputs(self.model_id)]
set_seed(0)
model.priors[0].cuda()
zs = [torch.zeros(1, 0, dtype=torch.long).cuda() for _ in range(3)]
zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
model.priors[0].cpu()
set_seed(0)
model.priors[1].cuda()
zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
model.priors[1].cpu()
set_seed(0)
model.priors[2].cuda()
zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))
@slow
def test_fp16_slow_sampling(self):
prior_id = "ArthurZ/jukebox_prior_0"
model = JukeboxPrior.from_pretrained(prior_id, min_duration=0).eval().half().to("cuda")
labels = self.prepare_inputs(prior_id)[0].cuda()
metadata = model.get_metadata(labels, 0, 7680, 0)
set_seed(0)
outputs = model.sample(1, metadata=metadata, sample_tokens=60)
self.assertIn(outputs[0].cpu().tolist(), [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])
| 18,315 | 45.13602 | 115 | py |
transformers | transformers-main/tests/models/markuplm/test_modeling_markuplm.py | # coding=utf-8
# Copyright 2022 The Hugging Face Team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MarkupLMForQuestionAnswering,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMModel,
)
# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
class MarkupLMModelTester:
"""You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester"""
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
max_xpath_tag_unit_embeddings=20,
max_xpath_subs_unit_embeddings=30,
tag_pad_id=2,
subs_pad_id=2,
max_depth=10,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
self.tag_pad_id = tag_pad_id
self.subs_pad_id = subs_pad_id
self.max_depth = max_depth
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
xpath_tags_seq = ids_tensor(
[self.batch_size, self.seq_length, self.max_depth], self.max_xpath_tag_unit_embeddings
)
xpath_subs_seq = ids_tensor(
[self.batch_size, self.seq_length, self.max_depth], self.max_xpath_subs_unit_embeddings
)
input_mask = None
if self.use_input_mask:
input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
config = self.get_config()
return (
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
)
def get_config(self):
return MarkupLMConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings,
max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings,
tag_pad_id=self.tag_pad_id,
subs_pad_id=self.subs_pad_id,
max_depth=self.max_depth,
)
def create_and_check_model(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = MarkupLMModel(config=config)
model.to(torch_device)
model.eval()
print("Configs:", model.config.tag_pad_id, model.config.subs_pad_id)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_sequence_classification(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
config.num_labels = self.num_labels
model = MarkupLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
config.num_labels = self.num_labels
model = MarkupLMForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_question_answering(
self,
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
):
model = MarkupLMForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
xpath_tags_seq=xpath_tags_seq,
xpath_subs_seq=xpath_subs_seq,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
xpath_tags_seq,
xpath_subs_seq,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"xpath_tags_seq": xpath_tags_seq,
"xpath_subs_seq": xpath_subs_seq,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MarkupLMModel,
MarkupLMForSequenceClassification,
MarkupLMForTokenClassification,
MarkupLMForQuestionAnswering,
)
if is_torch_available()
else None
)
pipeline_model_mapping = (
{
"feature-extraction": MarkupLMModel,
"question-answering": MarkupLMForQuestionAnswering,
"text-classification": MarkupLMForSequenceClassification,
"token-classification": MarkupLMForTokenClassification,
"zero-shot": MarkupLMForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
# (batch of pretokenized examples).
return True
def setUp(self):
self.model_tester = MarkupLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=MarkupLMConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def prepare_html_string():
html_string = """
<!DOCTYPE html>
<html>
<head>
<title>Page Title</title>
</head>
<body>
<h1>This is a Heading</h1>
<p>This is a paragraph.</p>
</body>
</html>
"""
return html_string
@require_torch
class MarkupLMModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processor(self):
# TODO use from_pretrained here
feature_extractor = MarkupLMFeatureExtractor()
tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
return MarkupLMProcessor(feature_extractor, tokenizer)
@slow
def test_forward_pass_no_head(self):
model = MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(torch_device)
processor = self.default_processor
inputs = processor(prepare_html_string(), return_tensors="pt")
inputs = inputs.to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the last hidden states
expected_shape = torch.Size([1, 14, 768])
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
| 13,117 | 33.072727 | 117 | py |
transformers | transformers-main/tests/models/markuplm/test_tokenization_markuplm.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import List
from transformers import (
AddedToken,
MarkupLMTokenizerFast,
SpecialTokensMixin,
is_tf_available,
is_torch_available,
logging,
)
from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES, MarkupLMTokenizer
from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow
from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings
logger = logging.get_logger(__name__)
@require_tokenizers
class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = MarkupLMTokenizer
rust_tokenizer_class = MarkupLMTokenizerFast
test_rust_tokenizer = True
from_pretrained_kwargs = {"cls_token": "<s>"}
test_seq2seq = False
def setUp(self):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
# fmt: off
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "<unk>",] # noqa
# fmt: on
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3}
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps({"tags_dict": self.tags_dict}))
def get_nodes_and_xpaths(self):
nodes = ["hello", "world"]
xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
return nodes, xpaths
def get_nodes_and_xpaths_batch(self):
nodes = [["hello world", "running"], ["hello my name is bob"]]
xpaths = [
["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
["/html/body/div/li[2]/div/span"],
]
return nodes, xpaths
def get_question_nodes_and_xpaths(self):
question = "what's his name?"
nodes = ["hello world"]
xpaths = ["/html/body/div/li[1]/div/span"] # , "/html/body/div/li[1]/div/span"]
return question, nodes, xpaths
def get_question_nodes_and_xpaths_batch(self):
questions = ["what's his name?", "how is he called?"]
nodes = [["hello world", "running"], ["hello my name is bob"]]
xpaths = [
["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
["/html/body/div/li[2]/div/span"],
]
return questions, nodes, xpaths
def get_input_output_texts(self, tokenizer):
input_text = "UNwant\u00E9d,running"
output_text = "unwanted, running"
return input_text, output_text
def test_add_special_tokens(self):
tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
special_token = "[SPECIAL_TOKEN]"
special_token_xpath = "/html/body/div/li[1]/div/span"
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(
[special_token], xpaths=[special_token_xpath], add_special_tokens=False
)
self.assertEqual(len(encoded_special_token), 1)
decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_add_tokens_tokenizer(self):
tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
self.assertNotEqual(vocab_size, 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaa", "bbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
nodes = "aaaaa bbbbbb low cccccccccdddddddd l".split()
xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]
tokens = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
nodes = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split()
xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]
tokens = tokenizer.encode(
nodes,
xpaths=xpaths,
add_special_tokens=False,
)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-2], tokens[-3])
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-2], tokenizer.pad_token_id)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)]
tokenizer.add_tokens(new_toks)
input = "[ABC][DEF][ABC][DEF]"
if self.space_between_special_tokens:
output = "[ABC] [DEF] [ABC] [DEF]"
else:
output = input
encoded = tokenizer.encode(input.split(), xpaths=xpaths, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
@unittest.skip("Not implemented")
def test_right_and_left_truncation(self):
pass
def test_encode_plus_with_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, nodes)
padding_size = 10
padding_idx = tokenizer.pad_token_id
encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_special_tokens_mask=True)
input_ids = encoded_sequence["input_ids"]
special_tokens_mask = encoded_sequence["special_tokens_mask"]
sequence_length = len(input_ids)
# Test 'longest' and 'no_padding' don't do anything
tokenizer.padding_side = "right"
not_padded_sequence = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
padding=False,
return_special_tokens_mask=True,
)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
self.assertTrue(sequence_length == not_padded_sequence_length)
self.assertTrue(input_ids == not_padded_input_ids)
self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)
not_padded_sequence = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
padding=False,
return_special_tokens_mask=True,
)
not_padded_input_ids = not_padded_sequence["input_ids"]
not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"]
not_padded_sequence_length = len(not_padded_input_ids)
self.assertTrue(sequence_length == not_padded_sequence_length)
self.assertTrue(input_ids == not_padded_input_ids)
self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask)
# Test right padding
tokenizer.padding_side = "right"
right_padded_sequence = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
max_length=sequence_length + padding_size,
padding="max_length",
return_special_tokens_mask=True,
)
right_padded_input_ids = right_padded_sequence["input_ids"]
right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
right_padded_sequence_length = len(right_padded_input_ids)
self.assertTrue(sequence_length + padding_size == right_padded_sequence_length)
self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids)
self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask)
# Test left padding
tokenizer.padding_side = "left"
left_padded_sequence = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
max_length=sequence_length + padding_size,
padding="max_length",
return_special_tokens_mask=True,
)
left_padded_input_ids = left_padded_sequence["input_ids"]
left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
left_padded_sequence_length = len(left_padded_input_ids)
self.assertTrue(sequence_length + padding_size == left_padded_sequence_length)
self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids)
self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask)
if "token_type_ids" in tokenizer.model_input_names:
token_type_ids = encoded_sequence["token_type_ids"]
left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
assert token_type_ids + [0] * padding_size == right_padded_token_type_ids
assert [0] * padding_size + token_type_ids == left_padded_token_type_ids
if "attention_mask" in tokenizer.model_input_names:
attention_mask = encoded_sequence["attention_mask"]
right_padded_attention_mask = right_padded_sequence["attention_mask"]
left_padded_attention_mask = left_padded_sequence["attention_mask"]
self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask)
self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask)
def test_internal_consistency(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
tokens = []
for word in nodes:
tokens.extend(tokenizer.tokenize(word))
ids = tokenizer.convert_tokens_to_ids(tokens)
ids_2 = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
self.assertListEqual(ids, ids_2)
tokens_2 = tokenizer.convert_ids_to_tokens(ids)
self.assertNotEqual(len(tokens_2), 0)
text_2 = tokenizer.decode(ids)
self.assertIsInstance(text_2, str)
def test_mask_output(self):
tokenizers = self.get_tokenizers(fast=False, do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
if (
tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
and "token_type_ids" in tokenizer.model_input_names
):
information = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
sequences, mask = information["input_ids"], information["token_type_ids"]
self.assertEqual(len(sequences), len(mask))
def test_number_of_added_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# test 1: single sequence
nodes, xpaths = self.get_nodes_and_xpaths()
sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
attached_sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True)
# Method is implemented (e.g. not GPT-2)
if len(attached_sequences) != 2:
self.assertEqual(
tokenizer.num_special_tokens_to_add(pair=False), len(attached_sequences) - len(sequences)
)
# test 2: two sequences
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=False)
attached_sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=True)
# Method is implemented (e.g. not GPT-2)
if len(attached_sequences) != 2:
self.assertEqual(
tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences)
)
def test_padding_to_max_length(self):
"""We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated"""
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
padding_size = 10
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, nodes)
padding_idx = tokenizer.pad_token_id
# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
sequence_length = len(encoded_sequence)
# FIXME: the next line should be padding(max_length) to avoid warning
padded_sequence = tokenizer.encode(
nodes, xpaths=xpaths, max_length=sequence_length + padding_size, pad_to_max_length=True
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
# Check that nothing is done when a maximum length is not specified
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, pad_to_max_length=True)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
def test_padding(self, max_length=50):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
# Encode - Simple input
nodes, xpaths = self.get_nodes_and_xpaths()
input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(nodes, xpaths=xpaths, padding="longest")
input_p = tokenizer_p.encode(nodes, xpaths=xpaths, padding=True)
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode - Pair input
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
input_r = tokenizer_r.encode(
question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode(
question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(
question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode(
question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.encode(question, nodes, xpaths=xpaths, padding=True)
input_p = tokenizer_p.encode(question, nodes, xpaths=xpaths, padding="longest")
self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id)
# Encode_plus - Simple input
nodes, xpaths = self.get_nodes_and_xpaths()
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, padding="longest")
input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Encode_plus - Pair input
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
input_r = tokenizer_r.encode_plus(
question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
)
input_p = tokenizer_p.encode_plus(
question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(
question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
)
input_p = tokenizer_p.encode_plus(
question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length"
)
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
input_r = tokenizer_r.encode_plus(question, nodes, xpaths=xpaths, padding="longest")
input_p = tokenizer_p.encode_plus(question, nodes, xpaths=xpaths, padding=True)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"])
# Batch_encode_plus - Simple input
nodes, xpaths = self.get_nodes_and_xpaths_batch()
input_r = tokenizer_r.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
pad_to_max_length=True,
)
input_p = tokenizer_p.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
pad_to_max_length=True,
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
padding="longest",
)
input_p = tokenizer_p.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=max_length,
padding=True,
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, padding="longest")
input_p = tokenizer_p.batch_encode_plus(nodes, xpaths=xpaths, padding=True)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Batch_encode_plus - Pair input
questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()
input_r = tokenizer_r.batch_encode_plus(
list(zip(questions, nodes)),
is_pair=True,
xpaths=xpaths,
max_length=max_length,
truncation=True,
padding="max_length",
)
input_p = tokenizer_p.batch_encode_plus(
list(zip(questions, nodes)),
is_pair=True,
xpaths=xpaths,
max_length=max_length,
truncation=True,
padding="max_length",
)
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
input_r = tokenizer_r.batch_encode_plus(
list(zip(questions, nodes)),
is_pair=True,
xpaths=xpaths,
padding=True,
)
input_p = tokenizer_p.batch_encode_plus(
list(zip(questions, nodes)),
is_pair=True,
xpaths=xpaths,
padding="longest",
)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad on single examples after tokenization
nodes, xpaths = self.get_nodes_and_xpaths()
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
input_p = tokenizer_r.pad(input_p)
self.assert_padded_input_match(
input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id
)
# Using pad on single examples after tokenization
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id)
# Using pad after tokenization
nodes, xpaths = self.get_nodes_and_xpaths_batch()
input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
input_r = tokenizer_r.pad(input_r)
input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
input_p = tokenizer_r.pad(input_p)
self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id)
# Using pad after tokenization
nodes, xpaths = self.get_nodes_and_xpaths_batch()
input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length")
input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length")
self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Test not batched
nodes, xpaths = self.get_nodes_and_xpaths()
encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test not batched pairs
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths)
encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
# Test batched
nodes, xpaths = self.get_nodes_and_xpaths_batch()
encoded_sequences_1 = tokenizer.batch_encode_plus(nodes, is_pair=False, xpaths=xpaths)
encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths)
self.assertEqual(encoded_sequences_1, encoded_sequences_2)
def test_batch_encode_plus_batch_sequence_length(self):
# Tests that all encoded values have the correct size
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths_batch()
encoded_sequences = [
tokenizer.encode_plus(nodes_example, xpaths=xpaths_example)
for nodes_example, xpaths_example in zip(nodes, xpaths)
]
encoded_sequences_batch = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, padding=False
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
maximum_length = len(
max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
)
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, nodes)
encoded_sequences_padded = [
tokenizer.encode_plus(
nodes_example, xpaths=xpaths_example, max_length=maximum_length, padding="max_length"
)
for nodes_example, xpaths_example in zip(nodes, xpaths)
]
encoded_sequences_batch_padded = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, padding=True
)
self.assertListEqual(
encoded_sequences_padded,
self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded),
)
# check 'longest' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, padding=True
)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding="longest"
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key],
encoded_sequences_batch_padded_2[key],
)
# check 'no_padding' is unsensitive to a max length
encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, padding=False
)
encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding=False
)
for key in encoded_sequences_batch_padded_1.keys():
self.assertListEqual(
encoded_sequences_batch_padded_1[key],
encoded_sequences_batch_padded_2[key],
)
@unittest.skip("batch_encode_plus does not handle overflowing tokens.")
def test_batch_encode_plus_overflowing_tokens(self):
pass
def test_batch_encode_plus_padding(self):
# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
# Right padding tests
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths_batch()
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, nodes)
encoded_sequences = [
tokenizer.encode_plus(
nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
)
for nodes_example, xpaths_example in zip(nodes, xpaths)
]
encoded_sequences_batch = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
# Left padding tests
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokenizer.padding_side = "left"
nodes, xpaths = self.get_nodes_and_xpaths_batch()
max_length = 100
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, nodes)
encoded_sequences = [
tokenizer.encode_plus(
nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length"
)
for nodes_example, xpaths_example in zip(nodes, xpaths)
]
encoded_sequences_batch = tokenizer.batch_encode_plus(
nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length"
)
self.assertListEqual(
encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
)
def test_padding_to_multiple_of(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.pad_token is None:
self.skipTest("No padding token.")
else:
nodes, xpaths = self.get_nodes_and_xpaths()
# empty_tokens = tokenizer([""], [[]], padding=True, pad_to_multiple_of=8)
normal_tokens = tokenizer(nodes, xpaths=xpaths, padding=True, pad_to_multiple_of=8)
# for key, value in empty_tokens.items():
# self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
normal_tokens = tokenizer(nodes, xpaths=xpaths, pad_to_multiple_of=8)
for key, value in normal_tokens.items():
self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# Should also work with truncation
normal_tokens = tokenizer(
nodes, xpaths=xpaths, padding=True, truncation=True, pad_to_multiple_of=8
)
for key, value in normal_tokens.items():
self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
# truncation to something which is not a multiple of pad_to_multiple_of raises an error
self.assertRaises(
ValueError,
tokenizer.__call__,
nodes,
xpaths=xpaths,
padding=True,
truncation=True,
max_length=12,
pad_to_multiple_of=8,
)
def test_tokenizer_slow_store_full_signature(self):
signature = inspect.signature(self.tokenizer_class.__init__)
tokenizer = self.get_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty:
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_build_inputs_with_special_tokens(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
# Input tokens id
nodes, xpaths = self.get_nodes_and_xpaths()
input_simple = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False)
input_pair = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False)
# Generate output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
self.assertEqual(output_p, output_r)
# Generate pair output
output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
self.assertEqual(output_p, output_r)
def test_special_tokens_mask_input_pairs(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
add_special_tokens=True,
return_special_tokens_mask=True,
# add_prefix_space=False,
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
]
filtered_sequence = [x for x in filtered_sequence if x is not None]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_special_tokens_mask(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
# Testing single inputs
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
encoded_sequence_dict = tokenizer.encode_plus(
nodes, xpaths=xpaths, add_special_tokens=True, return_special_tokens_mask=True
)
encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
filtered_sequence = [x for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]]
self.assertEqual(encoded_sequence, filtered_sequence)
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
nodes, xpaths = self.get_nodes_and_xpaths()
tmpdirname = tempfile.mkdtemp()
before_tokens = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
after_vocab = after_tokenizer.get_vocab()
self.assertListEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
shutil.rmtree(tmpdirname)
def test_right_and_left_padding(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
sequence = "Sequence"
padding_size = 10
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer, sequence)
padding_idx = tokenizer.pad_token_id
# RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "right"
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(
nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert encoded_sequence + [padding_idx] * padding_size == padded_sequence
# LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True
tokenizer.padding_side = "left"
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
sequence_length = len(encoded_sequence)
padded_sequence = tokenizer.encode(
nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length"
)
padded_sequence_length = len(padded_sequence)
assert sequence_length + padding_size == padded_sequence_length
assert [padding_idx] * padding_size + encoded_sequence == padded_sequence
# RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding'
encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths)
sequence_length = len(encoded_sequence)
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, padding=True)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding="longest")
padded_sequence_left_length = len(padded_sequence_left)
assert sequence_length == padded_sequence_left_length
assert encoded_sequence == padded_sequence_left
tokenizer.padding_side = "right"
padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths)
padded_sequence_right_length = len(padded_sequence_right)
assert sequence_length == padded_sequence_right_length
assert encoded_sequence == padded_sequence_right
tokenizer.padding_side = "left"
padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding=False)
padded_sequence_left_length = len(padded_sequence_left)
assert sequence_length == padded_sequence_left_length
assert encoded_sequence == padded_sequence_left
def test_token_type_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# test 1: single sequence
nodes, xpaths = self.get_nodes_and_xpaths()
output = tokenizer(nodes, xpaths=xpaths, return_token_type_ids=True)
# Assert that the token type IDs have the same length as the input IDs
self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))
# Assert that the token type IDs have the same length as the attention mask
self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))
self.assertIn(0, output["token_type_ids"])
self.assertNotIn(1, output["token_type_ids"])
# test 2: two sequences (question + nodes)
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
output = tokenizer(question, nodes, xpaths, return_token_type_ids=True)
# Assert that the token type IDs have the same length as the input IDs
self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"]))
# Assert that the token type IDs have the same length as the attention mask
self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"]))
self.assertIn(0, output["token_type_ids"])
def test_offsets_mapping(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
text = ["a", "wonderful", "test"]
xpaths = ["html/body" for _ in range(len(text))]
# No pair
tokens_with_offsets = tokenizer_r.encode_plus(
text,
xpaths=xpaths,
return_special_tokens_mask=True,
return_offsets_mapping=True,
add_special_tokens=True,
)
added_tokens = tokenizer_r.num_special_tokens_to_add(False)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
# Pairs
text = "what's his name"
pair = ["a", "wonderful", "test"]
xpaths = ["html/body" for _ in range(len(pair))]
tokens_with_offsets = tokenizer_r.encode_plus(
text,
pair,
xpaths=xpaths,
return_special_tokens_mask=True,
return_offsets_mapping=True,
add_special_tokens=True,
)
added_tokens = tokenizer_r.num_special_tokens_to_add(True)
offsets = tokens_with_offsets["offset_mapping"]
# Assert there is the same number of tokens and offsets
self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
# Assert there is online added_tokens special_tokens
self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
return
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
return
model = model_class(config)
# Make sure the model contains at least the full vocabulary size in its embedding matrix
is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight")
assert (
(model.get_input_embeddings().weight.shape[0] >= len(tokenizer))
if is_using_common_embeddings
else True
)
# Build sequence
nodes, xpaths = self.get_nodes_and_xpaths()
encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_tensors="pt")
batch_encoded_sequence = tokenizer.batch_encode_plus(
[nodes, nodes], [xpaths, xpaths], return_tensors="pt"
)
# This should not fail
with torch.no_grad(): # saves some time
model(**encoded_sequence)
model(**batch_encoded_sequence)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
nodes, xpaths = self.get_nodes_and_xpaths()
ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True)
rust_ids = rust_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True)
self.assertListEqual(ids, rust_ids)
def test_tokenization_python_rust_equals(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
nodes, xpaths = self.get_nodes_and_xpaths()
# Ensure basic input match
input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
for key in filter(
lambda x: x
in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"],
input_p.keys(),
):
self.assertSequenceEqual(input_p[key], input_r[key])
input_pairs_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths)
input_pairs_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths)
for key in filter(
lambda x: x
in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"],
input_p.keys(),
):
self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key])
nodes = ["hello" for _ in range(1000)]
xpaths = ["html/body" for _ in range(1000)]
# Ensure truncation match
input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=512, truncation=True)
input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=512, truncation=True)
for key in filter(
lambda x: x
in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"],
input_p.keys(),
):
self.assertSequenceEqual(input_p[key], input_r[key])
# Ensure truncation with stride match
input_p = tokenizer_p.encode_plus(
nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
input_r = tokenizer_r.encode_plus(
nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True
)
for key in filter(
lambda x: x
in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"],
input_p.keys(),
):
self.assertSequenceEqual(input_p[key], input_r[key][0])
def test_embeded_special_tokens(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
nodes, xpaths = self.get_nodes_and_xpaths()
tokens_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
tokens_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
for key in tokens_p.keys():
self.assertEqual(tokens_r[key], tokens_p[key])
if "token_type_ids" in tokens_r:
self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
self.assertSequenceEqual(tokens_r, tokens_p)
def test_compare_add_special_tokens(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
simple_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False)
nodes, xpaths = self.get_nodes_and_xpaths()
# tokenize()
no_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=False)
with_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=True)
self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add)
# encode()
no_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, add_special_tokens=True)
self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add)
# encode_plus()
no_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=False)
with_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
for key in no_special_tokens.keys():
self.assertEqual(
len(no_special_tokens[key]),
len(with_special_tokens[key]) - simple_num_special_tokens_to_add,
)
# # batch_encode_plus
nodes, xpaths = self.get_nodes_and_xpaths_batch()
no_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=False)
with_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
for key in no_special_tokens.keys():
for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]):
self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add)
@slow
def test_markuplm_truncation_integration_test(self):
nodes, xpaths = self.get_nodes_and_xpaths()
tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", model_max_length=512)
for i in range(12, 512):
new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, max_length=i, truncation=True)
# Ensure that the input IDs are less than the max length defined.
self.assertLessEqual(len(new_encoded_inputs), i)
tokenizer.model_max_length = 20
new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)
dropped_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True)
# Ensure that the input IDs are still truncated when no max_length is specified
self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs)
self.assertLessEqual(len(new_encoded_inputs), 20)
@is_pt_tf_cross_test
def test_batch_encode_plus_tensors(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths_batch()
# A Tensor cannot be build by sequences which are not the same size
self.assertRaises(ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, return_tensors="pt")
self.assertRaises(ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, return_tensors="tf")
if tokenizer.pad_token_id is None:
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
nodes,
xpaths=xpaths,
padding=True,
return_tensors="pt",
)
self.assertRaises(
ValueError,
tokenizer.batch_encode_plus,
nodes,
xpaths=xpaths,
padding="longest",
return_tensors="tf",
)
else:
pytorch_tensor = tokenizer.batch_encode_plus(
nodes, xpaths=xpaths, padding=True, return_tensors="pt"
)
tensorflow_tensor = tokenizer.batch_encode_plus(
nodes, xpaths=xpaths, padding="longest", return_tensors="tf"
)
encoded_sequences = tokenizer.batch_encode_plus(nodes, xpaths=xpaths, padding=True)
for key in encoded_sequences.keys():
pytorch_value = pytorch_tensor[key].tolist()
tensorflow_value = tensorflow_tensor[key].numpy().tolist()
encoded_value = encoded_sequences[key]
self.assertEqual(pytorch_value, tensorflow_value, encoded_value)
def test_sequence_ids(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
if not tokenizer.is_fast:
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0 = "Test this method."
seq_1 = ["With", "these", "inputs."]
xpaths = ["html/body" for _ in range(len(seq_1))]
# We want to have sequence 0 and sequence 1 are tagged
# respectively with 0 and 1 token_ids
# (regardless of whether the model use token type ids)
# We use this assumption in the QA pipeline among other place
output = tokenizer(seq_0.split(), xpaths=xpaths)
self.assertIn(0, output.sequence_ids())
output = tokenizer(seq_0, seq_1, xpaths=xpaths)
self.assertIn(0, output.sequence_ids())
self.assertIn(1, output.sequence_ids())
if tokenizer.num_special_tokens_to_add(pair=True):
self.assertIn(None, output.sequence_ids())
def test_special_tokens_initialization(self):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
added_tokens = [AddedToken("<special>", lstrip=True)]
tokenizer_r = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
nodes = "Hey this is a <special> token".split()
xpaths = ["html/body" for _ in range(len(nodes))]
r_output = tokenizer_r.encode(nodes, xpaths=xpaths)
special_token_id = tokenizer_r.encode(["<special>"], xpaths=["html/body"], add_special_tokens=False)[0]
self.assertTrue(special_token_id in r_output)
if self.test_slow_tokenizer:
tokenizer_cr = self.rust_tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
tokenizer_p = self.tokenizer_class.from_pretrained(
pretrained_name, additional_special_tokens=added_tokens, **kwargs
)
nodes = "Hey this is a <special> token".split()
xpaths = ["html/body" for _ in range(len(nodes))]
p_output = tokenizer_p.encode(nodes, xpaths=xpaths)
cr_output = tokenizer_cr.encode(nodes, xpaths=xpaths)
self.assertEqual(p_output, r_output)
self.assertEqual(cr_output, r_output)
self.assertTrue(special_token_id in p_output)
self.assertTrue(special_token_id in cr_output)
def test_training_new_tokenizer(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
return
tokenizer = self.get_rust_tokenizer()
new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100)
# Test we can use the new tokenizer with something not seen during training
text = [["this", "is", "the"], ["how", "are", "you"]]
xpaths = [["html/body"] * 3, ["html/body"] * 3]
inputs = new_tokenizer(text, xpaths=xpaths)
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = ( # original expected result "this is the" seems contradicts to roberta-based tokenizer
"thisisthe"
)
if tokenizer.backend_tokenizer.normalizer is not None:
expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
self.assertEqual(expected_result, decoded_input)
# We check that the parameters of the tokenizer remained the same
# Check we have the same number of added_tokens for both pair and non-pair inputs.
self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False))
self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True))
# Check we have the correct max_length for both pair and non-pair inputs.
self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence)
self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair)
# Assert the set of special tokens match as we didn't ask to change them
self.assertSequenceEqual(
tokenizer.all_special_tokens_extended,
new_tokenizer.all_special_tokens_extended,
)
self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map)
def test_training_new_tokenizer_with_special_tokens_change(self):
# This feature only exists for fast tokenizers
if not self.test_rust_tokenizer:
return
tokenizer = self.get_rust_tokenizer()
# Test with a special tokens map
class_signature = inspect.signature(tokenizer.__class__)
if "cls_token" in class_signature.parameters:
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: "<cls>"}
)
cls_id = new_tokenizer.get_vocab()["<cls>"]
self.assertEqual(new_tokenizer.cls_token, "<cls>")
self.assertEqual(new_tokenizer.cls_token_id, cls_id)
# Create a new mapping from the special tokens defined in the original tokenizer
special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
special_tokens_list.remove("additional_special_tokens")
special_tokens_map = {}
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, f"_{token}") is not None:
special_token = getattr(tokenizer, token)
special_tokens_map[special_token] = f"{special_token}a"
# Train new tokenizer
new_tokenizer = tokenizer.train_new_from_iterator(
SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map
)
# Check the changes
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(tokenizer, f"_{token}") is None:
continue
special_token = getattr(tokenizer, token)
if special_token in special_tokens_map:
new_special_token = getattr(new_tokenizer, token)
self.assertEqual(special_tokens_map[special_token], new_special_token)
new_id = new_tokenizer.get_vocab()[new_special_token]
self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id)
# Check if the AddedToken / string format has been kept
for special_token in tokenizer.all_special_tokens_extended:
if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
)
elif isinstance(special_token, AddedToken):
# The special token must appear in the list of the new tokenizer as an object of type AddedToken with
# the same parameters as the old AddedToken except the content that the user has requested to change.
special_token_str = special_token.content
new_special_token_str = special_tokens_map[special_token_str]
find = False
for candidate in new_tokenizer.all_special_tokens_extended:
if (
isinstance(candidate, AddedToken)
and candidate.content == new_special_token_str
and candidate.lstrip == special_token.lstrip
and candidate.rstrip == special_token.rstrip
and candidate.normalized == special_token.normalized
and candidate.single_word == special_token.single_word
):
find = True
break
self.assertTrue(
find,
f"'{new_special_token_str}' doesn't appear in the list "
f"'{new_tokenizer.all_special_tokens_extended}' as an AddedToken with the same parameters as "
f"'{special_token}' in the list {tokenizer.all_special_tokens_extended}",
)
elif special_token not in special_tokens_map:
# The special token must appear identically in the list of the new tokenizer.
self.assertTrue(
special_token in new_tokenizer.all_special_tokens_extended,
f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}",
)
else:
# The special token must appear in the list of the new tokenizer as an object of type string.
self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended)
# Test we can use the new tokenizer with something not seen during training
nodes = [["this", "is"], ["hello", "🤗"]]
xpaths = [["html/body"] * 2, ["html/body"] * 2]
inputs = new_tokenizer(nodes, xpaths=xpaths)
self.assertEqual(len(inputs["input_ids"]), 2)
decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True)
expected_result = "thisis" # same as line 1399
if tokenizer.backend_tokenizer.normalizer is not None:
expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result)
self.assertEqual(expected_result, decoded_input)
def test_prepare_for_model(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
# only test prepare_for_model for the slow tokenizer
if tokenizer.__class__.__name__ == "MarkupLMTokenizerFast":
continue
with self.subTest(f"{tokenizer.__class__.__name__}"):
nodes, xpaths = self.get_nodes_and_xpaths()
prepared_input_dict = tokenizer.prepare_for_model(nodes, xpaths=xpaths, add_special_tokens=True)
input_dict = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True)
self.assertEqual(input_dict, prepared_input_dict)
def test_padding_different_model_input_name(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id)
pad_token_id = tokenizer_p.pad_token_id
nodes, xpaths = self.get_nodes_and_xpaths_batch()
input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths)
# rename encoded batch to "inputs"
input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]]
del input_r[tokenizer_r.model_input_names[0]]
input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]]
del input_p[tokenizer_p.model_input_names[0]]
# Renaming `input_ids` to `inputs`
tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:]
tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:]
input_r = tokenizer_r.pad(input_r, padding="longest")
input_p = tokenizer_r.pad(input_p, padding="longest")
max_length = len(input_p["inputs"][0])
self.assert_batch_padded_input_match(
input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs"
)
def test_batch_encode_dynamic_overflowing(self):
"""
When calling batch_encode with multiple sequences, it can return different number of
overflowing encoding for each sequence:
[
Sequence 1: [Encoding 1, Encoding 2],
Sequence 2: [Encoding 1],
Sequence 3: [Encoding 1, Encoding 2, ... Encoding N]
]
This needs to be padded so that it can represented as a tensor
"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"):
if is_torch_available():
returned_tensor = "pt"
elif is_tf_available():
returned_tensor = "tf"
else:
returned_tensor = "jax"
# Single example
nodes, xpaths = self.get_nodes_and_xpaths()
tokens = tokenizer.encode_plus(
nodes,
xpaths=xpaths,
max_length=1,
padding=True,
truncation=True,
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
if "xpath" not in key:
self.assertEqual(len(tokens[key].shape), 2)
else:
self.assertEqual(len(tokens[key].shape), 3)
# Batch of examples
# For these 2 examples, 3 training examples will be created
nodes, xpaths = self.get_nodes_and_xpaths_batch()
tokens = tokenizer.batch_encode_plus(
nodes,
xpaths=xpaths,
max_length=6,
padding=True,
truncation="only_first",
return_tensors=returned_tensor,
return_overflowing_tokens=True,
)
for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()):
if "xpath" not in key:
self.assertEqual(len(tokens[key].shape), 2)
self.assertEqual(tokens[key].shape[-1], 6)
else:
self.assertEqual(len(tokens[key].shape), 3)
self.assertEqual(tokens[key].shape[-2], 6)
@unittest.skip("TO DO: overwrite this very extensive test.")
def test_alignement_methods(self):
pass
def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5):
toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))]
toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
toks = list(
filter(
lambda t: [t[0]]
== tokenizer.encode(t[1].split(" "), xpaths=len(t[1]) * ["html/body"], add_special_tokens=False),
toks,
)
)
if max_length is not None and len(toks) > max_length:
toks = toks[:max_length]
if min_length is not None and len(toks) < min_length and len(toks) > 0:
while len(toks) < min_length:
toks = toks + toks
# toks_str = [t[1] for t in toks]
toks_ids = [t[0] for t in toks]
# Ensure consistency
output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
# an extra blank will cause inconsistency: ["a","b",] & "a b"
"""
if " " not in output_txt and len(toks_ids) > 1:
output_txt = (
tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
+ " "
+ tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
)
"""
if with_prefix_space:
output_txt = " " + output_txt
nodes = output_txt.split(" ")
xpaths = ["html/body" for i in range(len(nodes))]
output_ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False)
return nodes, xpaths, output_ids
def test_maximum_encoding_length_pair_input(self):
# slow part fixed, fast part not
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Build a sequence from our model's vocabulary
stride = 2
seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
question_0 = " ".join(map(str, seq_0))
if len(ids) <= 2 + stride:
seq_0 = (seq_0 + " ") * (2 + stride)
ids = None
seq0_tokens = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
self.assertGreater(len(seq0_tokens["input_ids"]), 2 + stride)
question_1 = "This is another sentence to be encoded."
seq_1 = ["hello", "world"]
xpaths_1 = ["html/body" for i in range(len(seq_1))]
seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
if abs(len(seq0_tokens["input_ids"]) - len(seq1_tokens["input_ids"])) <= 2:
seq1_tokens_input_ids = seq1_tokens["input_ids"] + seq1_tokens["input_ids"]
seq_1 = tokenizer.decode(seq1_tokens_input_ids, clean_up_tokenization_spaces=False)
seq_1 = seq_1.split(" ")
xpaths_1 = ["html/body" for i in range(len(seq_1))]
seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
self.assertGreater(len(seq1_tokens["input_ids"]), 2 + stride)
smallest = (
seq1_tokens["input_ids"]
if len(seq0_tokens["input_ids"]) > len(seq1_tokens["input_ids"])
else seq0_tokens["input_ids"]
)
# We are not using the special tokens - a bit too hard to test all the tokenizers with this
# TODO try this again later
sequence = tokenizer(question_0, seq_1, xpaths=xpaths_1, add_special_tokens=False)
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_2 = seq_0 * model_max_length
question_2 = " ".join(map(str, seq_2))
xpaths_2 = xpaths_0 * model_max_length
# assertgreater -> assertgreaterequal
self.assertGreaterEqual(len(seq_2), model_max_length)
sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
sequence2 = tokenizer(question_2, seq_1, xpaths=xpaths_1, add_special_tokens=False)
total_length2 = len(sequence2["input_ids"])
self.assertLess(total_length1, model_max_length, "Issue with the testing sequence, please update it.")
self.assertGreater(
total_length2, model_max_length, "Issue with the testing sequence, please update it."
)
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
output = tokenizer(
question_2,
seq_1,
xpaths=xpaths_1,
padding=padding_state,
truncation=truncation_state,
)
self.assertEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)
output = tokenizer(
[question_2],
[seq_1],
xpaths=[xpaths_1],
padding=padding_state,
truncation=truncation_state,
)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)
# Simple
output = tokenizer(
question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation="only_second"
)
self.assertEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)
output = tokenizer(
[question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation="only_second"
)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(
question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation=False
)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(
[question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation=False
)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
# Check the order of Sequence of input ids, overflowing tokens and xpath_tags_seq sequence with truncation
truncated_first_sequence = (
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][:-2]
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
)
truncated_second_sequence = (
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][:-2]
)
truncated_longest_sequence = (
truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence
)
overflow_first_sequence = (
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][-(2 + stride) :]
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"]
)
overflow_second_sequence = (
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][-(2 + stride) :]
)
overflow_longest_sequence = (
overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence
)
xpath_tags_seq_first = [[5] * 50] * (
len(tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]) - 2
)
xpath_tags_seq_first_sequence = (
xpath_tags_seq_first
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"]
)
overflowing_token_xpath_tags_seq_first_sequence_slow = [[5] * 50] * (2 + stride)
overflowing_token_xpath_tags_seq_first_sequence_fast = [[5] * 50] * (2 + stride) + tokenizer(
seq_1, xpaths=xpaths_1, add_special_tokens=False
)["xpath_tags_seq"]
xpath_tags_seq_second = [[5] * 50] * len(
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
)
xpath_tags_seq_second_sequence = (
xpath_tags_seq_second
+ tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][:-2]
)
overflowing_token_xpath_tags_seq_second_sequence_slow = tokenizer(
seq_1, xpaths=xpaths_1, add_special_tokens=False
)["xpath_tags_seq"][-(2 + stride) :]
overflowing_token_xpath_tags_seq_second_sequence_fast = [[5] * 50] * len(
tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]
) + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][-(2 + stride) :]
xpath_tags_seq_longest_sequence = (
xpath_tags_seq_first_sequence
if len(seq0_tokens) > len(seq1_tokens)
else xpath_tags_seq_second_sequence
)
overflowing_token_xpath_tags_seq_longest_sequence_fast = (
overflowing_token_xpath_tags_seq_first_sequence_fast
if len(seq0_tokens) > len(seq1_tokens)
else overflowing_token_xpath_tags_seq_second_sequence_fast
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, MarkupLMTokenizerFast):
information = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
xpath_tags_seq = information["xpath_tags_seq"][0]
overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
self.assertEqual(overflowing_tokens, overflow_longest_sequence)
self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)
self.assertEqual(len(overflowing_xpath_tags_seq), 2 + stride + len(smallest))
self.assertEqual(
overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
)
else:
# No overflowing tokens when using 'longest' in python tokenizers
with self.assertRaises(ValueError) as context:
information = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation="longest_first",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
self.assertTrue(
context.exception.args[0].startswith(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, MarkupLMTokenizerFast):
information = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation=True,
return_overflowing_tokens=True,
)
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
xpath_tags_seq = information["xpath_tags_seq"][0]
overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_longest_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest))
self.assertEqual(overflowing_tokens, overflow_longest_sequence)
self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence)
self.assertEqual(
overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast
)
else:
# No overflowing tokens when using 'longest' in python tokenizers
with self.assertRaises(ValueError) as context:
information = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation=True,
return_overflowing_tokens=True,
)
self.assertTrue(
context.exception.args[0].startswith(
"Not possible to return overflowing tokens for pair of sequences with the "
"`longest_first`. Please select another truncation strategy than `longest_first`, "
"for instance `only_second` or `only_first`."
)
)
information_first_truncated = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation="only_first",
return_overflowing_tokens=True,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, MarkupLMTokenizerFast):
truncated_sequence = information_first_truncated["input_ids"][0]
overflowing_tokens = information_first_truncated["input_ids"][1]
xpath_tags_seq = information_first_truncated["xpath_tags_seq"][0]
overflowing_xpath_tags_seq = information_first_truncated["xpath_tags_seq"][1]
self.assertEqual(len(information_first_truncated["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens["input_ids"]))
self.assertEqual(overflowing_tokens, overflow_first_sequence)
self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
# ISSUE HAPPENS HERE ↓
self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_fast)
else:
truncated_sequence = information_first_truncated["input_ids"]
overflowing_tokens = information_first_truncated["overflowing_tokens"]
overflowing_xpath_tags_seq = information_first_truncated["overflowing_xpath_tags_seq"]
xpath_tags_seq = information_first_truncated["xpath_tags_seq"]
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_first_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq0_tokens["input_ids"][-(2 + stride) :])
self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence)
self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_slow)
information_second_truncated = tokenizer(
question_0,
seq_1,
xpaths=xpaths_1,
max_length=len(sequence["input_ids"]) - 2,
add_special_tokens=False,
stride=stride,
truncation="only_second",
return_overflowing_tokens=True,
# add_prefix_space=False,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, MarkupLMTokenizerFast):
truncated_sequence = information_second_truncated["input_ids"][0]
overflowing_tokens = information_second_truncated["input_ids"][1]
xpath_tags_seq = information_second_truncated["xpath_tags_seq"][0]
overflowing_xpath_tags_seq = information_second_truncated["xpath_tags_seq"][1]
self.assertEqual(len(information_second_truncated["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_tokens["input_ids"]))
self.assertEqual(overflowing_tokens, overflow_second_sequence)
self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence)
self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_fast)
else:
truncated_sequence = information_second_truncated["input_ids"]
overflowing_tokens = information_second_truncated["overflowing_tokens"]
xpath_tags_seq = information_second_truncated["xpath_tags_seq"]
overflowing_xpath_tags_seq = information_second_truncated["overflowing_xpath_tags_seq"]
self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2)
self.assertEqual(truncated_sequence, truncated_second_sequence)
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, seq1_tokens["input_ids"][-(2 + stride) :])
self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence)
self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_slow)
def test_maximum_encoding_length_single_input(self):
tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
sequence = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)
total_length = len(sequence["input_ids"])
self.assertGreater(total_length, 4, "Issue with the testing sequence, please update it it's too short")
# Test with max model input length
model_max_length = tokenizer.model_max_length
self.assertEqual(model_max_length, 100)
seq_1 = seq_0 * model_max_length
xpaths_1 = xpaths_0 * model_max_length
sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)
total_length1 = len(sequence1["input_ids"])
self.assertGreater(
total_length1, model_max_length, "Issue with the testing sequence, please update it it's too short"
)
# Simple
padding_strategies = (
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
)
for padding_state in padding_strategies:
with self.subTest(f"Padding: {padding_state}"):
for truncation_state in [True, "longest_first", "only_first"]:
with self.subTest(f"Truncation: {truncation_state}"):
output = tokenizer(
seq_1,
xpaths=xpaths_1,
padding=padding_state,
truncation=truncation_state,
)
self.assertEqual(len(output["input_ids"]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"]), model_max_length)
output = tokenizer(
[seq_1],
xpaths=[xpaths_1],
padding=padding_state,
truncation=truncation_state,
)
self.assertEqual(len(output["input_ids"][0]), model_max_length)
self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length)
self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length)
# Simple with no truncation
# Reset warnings
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer(seq_1, xpaths=xpaths_1, padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"]), model_max_length)
self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length)
self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
tokenizer.deprecation_warnings = {}
with self.assertLogs("transformers", level="WARNING") as cm:
output = tokenizer([seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=False)
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length)
self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length)
self.assertEqual(len(cm.records), 1)
self.assertTrue(
cm.records[0].message.startswith(
"Token indices sequence length is longer than the specified maximum sequence length"
" for this model"
)
)
# Check the order of Sequence of input ids, overflowing tokens, xpath_tags_seq and xpath_subs_seq sequence with truncation
stride = 2
information = tokenizer(
seq_0,
xpaths=xpaths_0,
max_length=total_length - 2,
add_special_tokens=False,
stride=stride,
truncation=True,
return_overflowing_tokens=True,
)
# Overflowing tokens are handled quite differently in slow and fast tokenizers
if isinstance(tokenizer, MarkupLMTokenizerFast):
truncated_sequence = information["input_ids"][0]
overflowing_tokens = information["input_ids"][1]
xpath_tags_seq = information["xpath_tags_seq"][0]
overflowing_xpath_tags_seq = information["xpath_tags_seq"][1]
self.assertEqual(len(information["input_ids"]), 2)
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, sequence["input_ids"][:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])
self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])
else:
truncated_sequence = information["input_ids"]
overflowing_tokens = information["overflowing_tokens"]
xpath_tags_seq = information["xpath_tags_seq"]
overflowing_xpath_tags_seq = information["overflowing_xpath_tags_seq"]
self.assertEqual(len(truncated_sequence), total_length - 2)
self.assertEqual(truncated_sequence, sequence["input_ids"][:-2])
self.assertEqual(len(overflowing_tokens), 2 + stride)
self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :])
self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2])
self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :])
@unittest.skip("MarkupLM tokenizer requires xpaths besides sequences.")
def test_pretokenized_inputs(self):
pass
@unittest.skip("MarkupLM tokenizer always expects pretokenized inputs.")
def test_compare_pretokenized_inputs(self):
pass
@unittest.skip("MarkupLM fast tokenizer does not support prepare_for_model")
def test_compare_prepare_for_model(self):
pass
@slow
def test_only_label_first_subword(self):
nodes = ["hello", "niels"]
xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))]
node_labels = [0, 1]
# test slow tokenizer
tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])
tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels)
self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])
# test fast tokenizer
tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")
encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100])
tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False)
encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels)
self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100])
def test_markuplm_integration_test(self):
tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")
# There are 3 cases:
# CASE 1: document image classification (training + inference), document image token classification (inference),
# in which case only nodes and normalized bounding xpaths are provided to the tokenizer
# CASE 2: document image token classification (training),
# in which case one also provides word labels to the tokenizer
# CASE 3: document image visual question answering (inference),
# in which case one also provides a question to the tokenizer
# We need to test all 3 cases both on batched and non-batched inputs.
# CASE 1: not batched
nodes, xpaths = self.get_nodes_and_xpaths()
# fmt: off
expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: on
encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
# CASE 1: batched
nodes, xpaths = self.get_nodes_and_xpaths_batch()
# fmt: off
expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20)
encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
# CASE 2: not batched
nodes, xpaths = self.get_nodes_and_xpaths()
node_labels = [1, 2, 3]
# fmt: off
expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
# fmt: on
encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
# CASE 2: batched
nodes, xpaths = self.get_nodes_and_xpaths_batch()
node_labels = [[1, 2, 3], [2, 46, 17, 22, 3]]
# fmt: off
expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: on
encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
# CASE 3: not batched
question, nodes, xpaths = self.get_question_nodes_and_xpaths()
# fmt: off
expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
# fmt: on
encoding_p = tokenizer_p(question, nodes, xpaths, padding="max_length", max_length=20)
encoding_r = tokenizer_r(question, nodes, xpaths, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
# CASE 3: batched
questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch()
# fmt: off
expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 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1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
encoding_p = tokenizer_p(questions, nodes, xpaths, padding="max_length", max_length=20)
encoding_r = tokenizer_r(questions, nodes, xpaths, padding="max_length", max_length=20)
self.assertDictEqual(dict(encoding_p), expected_results)
self.assertDictEqual(dict(encoding_r), expected_results)
@unittest.skip("Doesn't support another framework than PyTorch")
def test_np_encode_plus_sent_to_model(self):
pass
def test_padding_warning_message_fast_tokenizer(self):
if not self.test_rust_tokenizer:
return
nodes, xpaths = self.get_nodes_and_xpaths()
tokenizer_fast = self.get_rust_tokenizer()
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer_fast, nodes)
encoding_fast = tokenizer_fast(nodes, xpaths=xpaths)
with self.assertLogs("transformers", level="WARNING") as cm:
tokenizer_fast.pad(encoding_fast)
self.assertEqual(len(cm.records), 1)
self.assertIn(
"Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
" encode the text followed by a call to the `pad` method to get a padded encoding.",
cm.records[0].message,
)
if not self.test_slow_tokenizer:
return
tokenizer_slow = self.get_tokenizer()
# check correct behaviour if no pad_token_id exists and add it eventually
self._check_no_pad_token_padding(tokenizer_slow, nodes)
encoding_slow = tokenizer_slow(nodes, xpaths=xpaths)
with self.assertLogs(level="WARNING") as cm:
# We want to assert there are no warnings, but the 'assertLogs' method does not support that.
# Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
logger.warning("Dummy warning")
tokenizer_slow.pad(encoding_slow)
self.assertEqual(len(cm.records), 1)
self.assertIn(
"Dummy warning",
cm.records[0].message,
)
| 220,966 | 95.030856 | 22,775 | py |
transformers | transformers-main/tests/models/markuplm/test_processor_markuplm.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import shutil
import tempfile
import unittest
from typing import List
from transformers import (
MarkupLMProcessor,
MarkupLMTokenizer,
PreTrainedTokenizer,
PreTrainedTokenizerBase,
PreTrainedTokenizerFast,
)
from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_bs4, require_tokenizers, require_torch, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_bs4_available, is_tokenizers_available
if is_bs4_available():
from transformers import MarkupLMFeatureExtractor
if is_tokenizers_available():
from transformers import MarkupLMTokenizerFast
@require_bs4
@require_tokenizers
class MarkupLMProcessorTest(unittest.TestCase):
tokenizer_class = MarkupLMTokenizer
rust_tokenizer_class = MarkupLMTokenizerFast
def setUp(self):
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
# fmt: off
vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "\u0120hello", "\u0120world", "<unk>",] # noqa
# fmt: on
self.tmpdirname = tempfile.mkdtemp()
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3}
self.special_tokens_map = {"unk_token": "<unk>"}
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json")
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(self.merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps({"tags_dict": self.tags_dict}))
feature_extractor_map = {"feature_extractor_type": "MarkupLMFeatureExtractor"}
self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(feature_extractor_map) + "\n")
def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer:
return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast:
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]:
return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)]
def get_feature_extractor(self, **kwargs):
return MarkupLMFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
feature_extractor = self.get_feature_extractor()
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.save_pretrained(self.tmpdirname)
processor = MarkupLMProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer, (MarkupLMTokenizer, MarkupLMTokenizerFast))
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = MarkupLMProcessor(feature_extractor=self.get_feature_extractor(), tokenizer=self.get_tokenizer())
processor.save_pretrained(self.tmpdirname)
# slow tokenizer
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30)
processor = MarkupLMProcessor.from_pretrained(
self.tmpdirname, use_fast=False, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, MarkupLMTokenizer)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor)
# fast tokenizer
tokenizer_add_kwargs = self.get_rust_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30)
processor = MarkupLMProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, MarkupLMTokenizerFast)
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor)
def test_model_input_names(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = MarkupLMProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
tokenizer.model_input_names,
msg="`processor` and `tokenizer` model input names do not match",
)
# different use cases tests
@require_bs4
@require_torch
class MarkupLMProcessorIntegrationTests(unittest.TestCase):
@cached_property
def get_html_strings(self):
html_string_1 = """
<!DOCTYPE html>
<html>
<head>
<title>Hello world</title>
</head>
<body>
<h1>Welcome</h1>
<p>Here is my website.</p>
</body>
</html>"""
html_string_2 = """
<!DOCTYPE html>
<html>
<body>
<h2>HTML Images</h2>
<p>HTML images are defined with the img tag:</p>
<img src="w3schools.jpg" alt="W3Schools.com" width="104" height="142">
</body>
</html>
"""
return [html_string_1, html_string_2]
@cached_property
def get_tokenizers(self):
slow_tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base")
fast_tokenizer = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", from_slow=True)
return [slow_tokenizer, fast_tokenizer]
@slow
def test_processor_case_1(self):
# case 1: web page classification (training, inference) + token classification (inference)
feature_extractor = MarkupLMFeatureExtractor()
tokenizers = self.get_tokenizers
html_strings = self.get_html_strings
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
# not batched
inputs = processor(html_strings[0], return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected = [0, 31414, 232, 25194, 11773, 16, 127, 998, 4, 2]
self.assertSequenceEqual(inputs.input_ids.squeeze().tolist(), expected)
# batched
inputs = processor(html_strings, padding=True, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected = [0, 48085, 2209, 48085, 3156, 32, 6533, 19, 5, 48599, 6694, 35, 2]
self.assertSequenceEqual(inputs.input_ids[1].tolist(), expected)
@slow
def test_processor_case_2(self):
# case 2: web page classification (training, inference) + token classification (inference), parse_html=False
feature_extractor = MarkupLMFeatureExtractor()
tokenizers = self.get_tokenizers
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.parse_html = False
# not batched
nodes = ["hello", "world", "how", "are"]
xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
inputs = processor(nodes=nodes, xpaths=xpaths, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = list(inputs.keys())
for key in expected_keys:
self.assertIn(key, actual_keys)
# verify input_ids
expected_decoding = "<s>helloworldhoware</s>"
decoding = processor.decode(inputs.input_ids.squeeze().tolist())
self.assertSequenceEqual(decoding, expected_decoding)
# batched
nodes = [["hello", "world"], ["my", "name", "is"]]
xpaths = [
["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
["html/body", "html/body/div", "html/body"],
]
inputs = processor(nodes=nodes, xpaths=xpaths, padding=True, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_decoding = "<s>helloworld</s><pad>"
decoding = processor.decode(inputs.input_ids[0].tolist())
self.assertSequenceEqual(decoding, expected_decoding)
@slow
def test_processor_case_3(self):
# case 3: token classification (training), parse_html=False
feature_extractor = MarkupLMFeatureExtractor()
tokenizers = self.get_tokenizers
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.parse_html = False
# not batched
nodes = ["hello", "world", "how", "are"]
xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
node_labels = [1, 2, 2, 1]
inputs = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")
# verify keys
expected_keys = [
"attention_mask",
"input_ids",
"labels",
"token_type_ids",
"xpath_subs_seq",
"xpath_tags_seq",
]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_ids = [0, 42891, 8331, 9178, 1322, 2]
self.assertSequenceEqual(inputs.input_ids[0].tolist(), expected_ids)
# verify labels
expected_labels = [-100, 1, 2, 2, 1, -100]
self.assertListEqual(inputs.labels.squeeze().tolist(), expected_labels)
# batched
nodes = [["hello", "world"], ["my", "name", "is"]]
xpaths = [
["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
["html/body", "html/body/div", "html/body"],
]
node_labels = [[1, 2], [6, 3, 10]]
inputs = processor(
nodes=nodes,
xpaths=xpaths,
node_labels=node_labels,
padding="max_length",
max_length=20,
truncation=True,
return_tensors="pt",
)
# verify keys
expected_keys = [
"attention_mask",
"input_ids",
"labels",
"token_type_ids",
"xpath_subs_seq",
"xpath_tags_seq",
]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_ids = [0, 4783, 13650, 354, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
self.assertSequenceEqual(inputs.input_ids[1].tolist(), expected_ids)
# verify xpath_tags_seq
# fmt: off
expected_xpaths_tags_seq = [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]] # noqa:
# fmt: on
self.assertSequenceEqual(inputs.xpath_tags_seq[1].tolist(), expected_xpaths_tags_seq)
# verify labels
# fmt: off
expected_labels = [-100, 6, 3, 10, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]
# fmt: on
self.assertListEqual(inputs.labels[1].tolist(), expected_labels)
@slow
def test_processor_case_4(self):
# case 4: question answering (inference), parse_html=True
feature_extractor = MarkupLMFeatureExtractor()
tokenizers = self.get_tokenizers
html_strings = self.get_html_strings
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
# not batched
question = "What's his name?"
inputs = processor(html_strings[0], questions=question, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
# fmt: off
expected_decoding = "<s>What's his name?</s>Hello worldWelcomeHere is my website.</s>" # noqa: E231
# fmt: on
decoding = processor.decode(inputs.input_ids.squeeze().tolist())
self.assertSequenceEqual(decoding, expected_decoding)
# batched
questions = ["How old is he?", "what's the time"]
inputs = processor(
html_strings,
questions=questions,
padding="max_length",
max_length=20,
truncation=True,
return_tensors="pt",
)
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_decoding = (
"<s>what's the time</s>HTML ImagesHTML images are defined with the img tag:</s><pad><pad>"
)
decoding = processor.decode(inputs.input_ids[1].tolist())
self.assertSequenceEqual(decoding, expected_decoding)
# verify xpath_subs_seq
# fmt: off
expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]
# fmt: on
self.assertListEqual(inputs.xpath_subs_seq[1].tolist(), expected_xpath_subs_seq)
@slow
def test_processor_case_5(self):
# case 5: question answering (inference), parse_html=False
feature_extractor = MarkupLMFeatureExtractor(parse_html=False)
tokenizers = self.get_tokenizers
for tokenizer in tokenizers:
processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
processor.parse_html = False
# not batched
question = "What's his name?"
nodes = ["hello", "world", "how", "are"]
xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
inputs = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_decoding = "<s>What's his name?</s>helloworldhoware</s>"
decoding = processor.decode(inputs.input_ids.squeeze().tolist())
self.assertSequenceEqual(decoding, expected_decoding)
# batched
questions = ["How old is he?", "what's the time"]
nodes = [["hello", "world"], ["my", "name", "is"]]
xpaths = [
["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"],
["html/body", "html/body/div", "html/body"],
]
inputs = processor(nodes=nodes, xpaths=xpaths, questions=questions, padding=True, return_tensors="pt")
# verify keys
expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"]
actual_keys = sorted(inputs.keys())
self.assertListEqual(actual_keys, expected_keys)
# verify input_ids
expected_decoding = "<s>How old is he?</s>helloworld</s>"
decoding = processor.decode(inputs.input_ids[0].tolist())
self.assertSequenceEqual(decoding, expected_decoding)
expected_decoding = "<s>what's the time</s>mynameis</s>"
decoding = processor.decode(inputs.input_ids[1].tolist())
self.assertSequenceEqual(decoding, expected_decoding)
# verify xpath_subs_seq
# fmt: off
expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]
# fmt: on
self.assertListEqual(inputs.xpath_subs_seq[1].tolist()[-5:], expected_xpath_subs_seq)
| 31,845 | 67.633621 | 5,979 | py |
transformers | transformers-main/tests/models/maskformer/test_modeling_maskformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MaskFormer model. """
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class MaskFormerModelTester:
def __init__(
self,
parent,
batch_size=2,
is_training=True,
use_auxiliary_loss=False,
num_queries=10,
num_channels=3,
min_size=32 * 4,
max_size=32 * 6,
num_labels=4,
mask_feature_size=32,
):
self.parent = parent
self.batch_size = batch_size
self.is_training = is_training
self.use_auxiliary_loss = use_auxiliary_loss
self.num_queries = num_queries
self.num_channels = num_channels
self.min_size = min_size
self.max_size = max_size
self.num_labels = num_labels
self.mask_feature_size = mask_feature_size
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
torch_device
)
pixel_mask = torch.ones([self.batch_size, self.min_size, self.max_size], device=torch_device)
mask_labels = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size], device=torch_device) > 0.5
).float()
class_labels = (torch.rand((self.batch_size, self.num_labels), device=torch_device) > 0.5).long()
config = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def get_config(self):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1],
),
decoder_config=DetrConfig(
decoder_ffn_dim=128,
num_queries=self.num_queries,
decoder_attention_heads=2,
d_model=self.mask_feature_size,
),
mask_feature_size=self.mask_feature_size,
fpn_feature_size=self.mask_feature_size,
num_channels=self.num_channels,
num_labels=self.num_labels,
)
def prepare_config_and_inputs_for_common(self):
config, pixel_values, pixel_mask, _, _ = self.prepare_config_and_inputs()
inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def check_output_hidden_state(self, output, config):
encoder_hidden_states = output.encoder_hidden_states
pixel_decoder_hidden_states = output.pixel_decoder_hidden_states
transformer_decoder_hidden_states = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(encoder_hidden_states), len(config.backbone_config.depths))
self.parent.assertTrue(len(pixel_decoder_hidden_states), len(config.backbone_config.depths))
self.parent.assertTrue(len(transformer_decoder_hidden_states), config.decoder_config.decoder_layers)
def create_and_check_maskformer_model(self, config, pixel_values, pixel_mask, output_hidden_states=False):
with torch.no_grad():
model = MaskFormerModel(config=config)
model.to(torch_device)
model.eval()
output = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
output = model(pixel_values, output_hidden_states=True)
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape,
(self.batch_size, self.num_queries, self.mask_feature_size),
)
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(output.encoder_last_hidden_state is not None)
if output_hidden_states:
self.check_output_hidden_state(output, config)
def create_and_check_maskformer_instance_segmentation_head_model(
self, config, pixel_values, pixel_mask, mask_labels, class_labels
):
model = MaskFormerForInstanceSegmentation(config=config)
model.to(torch_device)
model.eval()
def comm_check_on_output(result):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
self.parent.assertTrue(result.encoder_last_hidden_state is not None)
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape,
(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),
)
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape, (self.batch_size, self.num_queries, self.num_labels + 1)
)
with torch.no_grad():
result = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
result = model(pixel_values)
comm_check_on_output(result)
result = model(
pixel_values=pixel_values, pixel_mask=pixel_mask, mask_labels=mask_labels, class_labels=class_labels
)
comm_check_on_output(result)
self.parent.assertTrue(result.loss is not None)
self.parent.assertEqual(result.loss.shape, torch.Size([1]))
@require_torch
class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
is_encoder_decoder = False
test_pruning = False
test_head_masking = False
test_missing_keys = False
def setUp(self):
self.model_tester = MaskFormerModelTester(self)
self.config_tester = ConfigTester(self, config_class=MaskFormerConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
def test_maskformer_model(self):
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(config, **inputs, output_hidden_states=False)
def test_maskformer_instance_segmentation_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*config_and_inputs)
@unittest.skip(reason="MaskFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MaskFormer does not have a get_input_embeddings method")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MaskFormer is not a generative model")
def test_generate_without_input_ids(self):
pass
@unittest.skip(reason="MaskFormer does not use token embeddings")
def test_resize_tokens_embeddings(self):
pass
@require_torch_multi_gpu
@unittest.skip(
reason="MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"
)
def test_multi_gpu_data_parallel_forward(self):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@slow
def test_model_from_pretrained(self):
for model_name in ["facebook/maskformer-swin-small-coco"]:
model = MaskFormerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_with_labels(self):
size = (self.model_tester.min_size,) * 2
inputs = {
"pixel_values": torch.randn((2, 3, *size), device=torch_device),
"mask_labels": torch.randn((2, 10, *size), device=torch_device),
"class_labels": torch.zeros(2, 10, device=torch_device).long(),
}
model = MaskFormerForInstanceSegmentation(MaskFormerConfig()).to(torch_device)
outputs = model(**inputs)
self.assertTrue(outputs.loss is not None)
def test_hidden_states_output(self):
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(config, **inputs, output_hidden_states=True)
def test_attention_outputs(self):
config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device)
outputs = model(**inputs, output_attentions=True)
self.assertTrue(outputs.attentions is not None)
def test_training(self):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
model_class = self.all_model_classes[1]
config, pixel_values, pixel_mask, mask_labels, class_labels = self.model_tester.prepare_config_and_inputs()
model = model_class(config)
model.to(torch_device)
model.train()
loss = model(pixel_values, mask_labels=mask_labels, class_labels=class_labels).loss
loss.backward()
def test_retain_grad_hidden_states_attentions(self):
# only MaskFormerForInstanceSegmentation has the loss
model_class = self.all_model_classes[1]
config, pixel_values, pixel_mask, mask_labels, class_labels = self.model_tester.prepare_config_and_inputs()
config.output_hidden_states = True
config.output_attentions = True
model = model_class(config)
model.to(torch_device)
model.train()
outputs = model(pixel_values, mask_labels=mask_labels, class_labels=class_labels)
encoder_hidden_states = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
pixel_decoder_hidden_states = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
transformer_decoder_hidden_states = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
attentions = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=True)
self.assertIsNotNone(encoder_hidden_states.grad)
self.assertIsNotNone(pixel_decoder_hidden_states.grad)
self.assertIsNotNone(transformer_decoder_hidden_states.grad)
self.assertIsNotNone(attentions.grad)
TOLERANCE = 1e-4
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_vision
@slow
class MaskFormerModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-small-coco")
if is_vision_available()
else None
)
def test_inference_no_head(self):
model = MaskFormerModel.from_pretrained("facebook/maskformer-swin-small-coco").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(inputs_shape, (1, 3, 800, 1088))
with torch.no_grad():
outputs = model(**inputs)
expected_slice_hidden_state = torch.tensor(
[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]
).to(torch_device)
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
)
)
expected_slice_hidden_state = torch.tensor(
[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]
).to(torch_device)
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
)
)
expected_slice_hidden_state = torch.tensor(
[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]
).to(torch_device)
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3], expected_slice_hidden_state, atol=TOLERANCE
)
)
def test_inference_instance_segmentation_head(self):
model = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco")
.to(torch_device)
.eval()
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(inputs_shape, (1, 3, 800, 1088))
with torch.no_grad():
outputs = model(**inputs)
# masks_queries_logits
masks_queries_logits = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape,
(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4),
)
expected_slice = [
[-1.3737124, -1.7724937, -1.9364233],
[-1.5977281, -1.9867939, -2.1523695],
[-1.5795398, -1.9269832, -2.093942],
]
expected_slice = torch.tensor(expected_slice).to(torch_device)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE))
# class_queries_logits
class_queries_logits = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)
)
expected_slice = torch.tensor(
[
[1.6512e00, -5.2572e00, -3.3519e00],
[3.6169e-02, -5.9025e00, -2.9313e00],
[1.0766e-04, -7.7630e00, -5.1263e00],
]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_instance_segmentation_head_resnet_backbone(self):
model = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-resnet101-coco-stuff")
.to(torch_device)
.eval()
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").to(torch_device)
inputs_shape = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
# check size
self.assertEqual(inputs_shape, (1, 3, 800, 1088))
with torch.no_grad():
outputs = model(**inputs)
# masks_queries_logits
masks_queries_logits = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape,
(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4),
)
expected_slice = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
expected_slice = torch.tensor(expected_slice).to(torch_device)
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3], expected_slice, atol=TOLERANCE))
# class_queries_logits
class_queries_logits = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape, (1, model.config.decoder_config.num_queries, model.config.num_labels + 1)
)
expected_slice = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]
).to(torch_device)
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3], expected_slice, atol=TOLERANCE))
def test_with_segmentation_maps_and_loss(self):
model = (
MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-small-coco")
.to(torch_device)
.eval()
)
image_processor = self.default_image_processor
inputs = image_processor(
[np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))],
segmentation_maps=[np.zeros((384, 384)).astype(np.float32), np.zeros((384, 384)).astype(np.float32)],
return_tensors="pt",
)
inputs["pixel_values"] = inputs["pixel_values"].to(torch_device)
inputs["mask_labels"] = [el.to(torch_device) for el in inputs["mask_labels"]]
inputs["class_labels"] = [el.to(torch_device) for el in inputs["class_labels"]]
with torch.no_grad():
outputs = model(**inputs)
self.assertTrue(outputs.loss is not None)
| 19,881 | 40.163561 | 117 | py |
transformers | transformers-main/tests/models/maskformer/test_modeling_maskformer_swin.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MaskFormer Swin model. """
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class MaskFormerSwinModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 2, 4],
window_size=2,
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=True,
type_sequence_label_size=10,
encoder_stride=8,
out_features=["stage1", "stage2", "stage3"],
out_indices=[1, 2, 3],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.type_sequence_label_size = type_sequence_label_size
self.encoder_stride = encoder_stride
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return MaskFormerSwinConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
window_size=self.window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
use_absolute_embeddings=self.use_absolute_embeddings,
path_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
encoder_stride=self.encoder_stride,
out_features=self.out_features,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels):
model = MaskFormerSwinModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim))
def create_and_check_backbone(self, config, pixel_values, labels):
model = MaskFormerSwinBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [13, 16, 16, 16])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, [16, 32, 64])
# verify ValueError
with self.parent.assertRaises(ValueError):
config.out_features = ["stem"]
model = MaskFormerSwinBackbone(config=config)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MaskFormerSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
fx_compatible = False
test_torchscript = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = MaskFormerSwinModelTester(self)
self.config_tester = ConfigTester(self, config_class=MaskFormerSwinConfig, embed_dim=37)
@require_torch_multi_gpu
@unittest.skip(
reason=(
"`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
" `nn.DataParallel`"
)
)
def test_multi_gpu_data_parallel_forward(self):
pass
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
@unittest.skip("Swin does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip("Swin does not support feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone")
def test_save_load_fast_init_to_base(self):
pass
def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# Swin has a different seq_length
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[num_patches, self.model_tester.embed_dim],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
def test_hidden_states_output_with_padding(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.patch_size = 3
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width))
@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints")
def test_model_from_pretrained(self):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
def test_initialization(self):
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin")
def test_gradient_checkpointing_backward_compatibility(self):
pass
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
@require_torch
class MaskFormerSwinBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (MaskFormerSwinBackbone,) if is_torch_available() else ()
config_class = MaskFormerSwinConfig
def setUp(self):
self.model_tester = MaskFormerSwinModelTester(self)
# Overriding as returned hidden states are tuples of tensors instead of a single tensor
def test_backbone_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
batch_size = inputs_dict["pixel_values"].shape[0]
for backbone_class in self.all_model_classes:
backbone = backbone_class(config)
backbone.to(torch_device)
backbone.eval()
outputs = backbone(**inputs_dict)
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps, tuple)
self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels):
self.assertTrue(feature_map.shape[:2], (batch_size, n_channels))
self.assertIsNone(outputs.hidden_states)
self.assertIsNone(outputs.attentions)
# Test output_hidden_states=True
outputs = backbone(**inputs_dict, output_hidden_states=True)
self.assertIsNotNone(outputs.hidden_states)
self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names))
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:], backbone.channels):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
h_batch_size, _, h_n_channels = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels), (batch_size, n_channels))
# Test output_attentions=True
if self.has_attentions:
outputs = backbone(**inputs_dict, output_attentions=True)
self.assertIsNotNone(outputs.attentions)
| 18,527 | 40.449664 | 118 | py |
transformers | transformers-main/tests/models/maskformer/test_image_processing_maskformer.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import MaskFormerImageProcessor
from transformers.models.maskformer.image_processing_maskformer import binary_mask_to_rle
from transformers.models.maskformer.modeling_maskformer import MaskFormerForInstanceSegmentationOutput
if is_vision_available():
from PIL import Image
class MaskFormerImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
min_resolution=30,
max_resolution=400,
size=None,
do_resize=True,
do_normalize=True,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
num_labels=10,
do_reduce_labels=True,
ignore_index=255,
):
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.size_divisor = 0
# for the post_process_functions
self.batch_size = 2
self.num_queries = 3
self.num_classes = 2
self.height = 3
self.width = 4
self.num_labels = num_labels
self.do_reduce_labels = do_reduce_labels
self.ignore_index = ignore_index
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"size_divisor": self.size_divisor,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
}
def get_expected_values(self, image_inputs, batched=False):
"""
This function computes the expected height and width when providing images to MaskFormerImageProcessor,
assuming do_resize is set to True with a scalar size.
"""
if not batched:
image = image_inputs[0]
if isinstance(image, Image.Image):
w, h = image.size
else:
h, w = image.shape[1], image.shape[2]
if w < h:
expected_height = int(self.size["shortest_edge"] * h / w)
expected_width = self.size["shortest_edge"]
elif w > h:
expected_height = self.size["shortest_edge"]
expected_width = int(self.size["shortest_edge"] * w / h)
else:
expected_height = self.size["shortest_edge"]
expected_width = self.size["shortest_edge"]
else:
expected_values = []
for image in image_inputs:
expected_height, expected_width = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
expected_height = max(expected_values, key=lambda item: item[0])[0]
expected_width = max(expected_values, key=lambda item: item[1])[1]
return expected_height, expected_width
def get_fake_maskformer_outputs(self):
return MaskFormerForInstanceSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)),
masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)),
)
@require_torch
@require_vision
class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = MaskFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
def setUp(self):
self.image_processor_tester = MaskFormerImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "image_mean"))
self.assertTrue(hasattr(image_processing, "image_std"))
self.assertTrue(hasattr(image_processing, "do_normalize"))
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "ignore_index"))
self.assertTrue(hasattr(image_processing, "num_labels"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 32, "longest_edge": 1333})
self.assertEqual(image_processor.size_divisor, 0)
image_processor = self.image_processing_class.from_dict(
self.image_processor_dict, size=42, max_size=84, size_divisibility=8
)
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.size_divisor, 8)
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs)
self.assertEqual(
encoded_images.shape,
(1, self.image_processor_tester.num_channels, expected_height, expected_width),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
expected_height, expected_width = self.image_processor_tester.get_expected_values(image_inputs, batched=True)
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
),
)
def comm_get_image_processing_inputs(
self, with_segmentation_maps=False, is_instance_map=False, segmentation_type="np"
):
image_processing = self.image_processing_class(**self.image_processor_dict)
# prepare image and target
num_labels = self.image_processor_tester.num_labels
annotations = None
instance_id_to_semantic_id = None
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
if with_segmentation_maps:
high = num_labels
if is_instance_map:
labels_expanded = list(range(num_labels)) * 2
instance_id_to_semantic_id = dict(enumerate(labels_expanded))
annotations = [
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
]
if segmentation_type == "pil":
annotations = [Image.fromarray(annotation) for annotation in annotations]
inputs = image_processing(
image_inputs,
annotations,
return_tensors="pt",
instance_id_to_semantic_id=instance_id_to_semantic_id,
pad_and_return_pixel_mask=True,
)
return inputs
def test_init_without_params(self):
pass
def test_with_size_divisor(self):
size_divisors = [8, 16, 32]
weird_input_sizes = [(407, 802), (582, 1094)]
for size_divisor in size_divisors:
image_processor_dict = {**self.image_processor_dict, **{"size_divisor": size_divisor}}
image_processing = self.image_processing_class(**image_processor_dict)
for weird_input_size in weird_input_sizes:
inputs = image_processing([np.ones((3, *weird_input_size))], return_tensors="pt")
pixel_values = inputs["pixel_values"]
# check if divisible
self.assertTrue((pixel_values.shape[-1] % size_divisor) == 0)
self.assertTrue((pixel_values.shape[-2] % size_divisor) == 0)
def test_call_with_segmentation_maps(self):
def common(is_instance_map=False, segmentation_type=None):
inputs = self.comm_get_image_processing_inputs(
with_segmentation_maps=True, is_instance_map=is_instance_map, segmentation_type=segmentation_type
)
mask_labels = inputs["mask_labels"]
class_labels = inputs["class_labels"]
pixel_values = inputs["pixel_values"]
# check the batch_size
for mask_label, class_label in zip(mask_labels, class_labels):
self.assertEqual(mask_label.shape[0], class_label.shape[0])
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:], pixel_values.shape[2:])
common()
common(is_instance_map=True)
common(is_instance_map=False, segmentation_type="pil")
common(is_instance_map=True, segmentation_type="pil")
def test_integration_instance_segmentation(self):
# load 2 images and corresponding annotations from the hub
repo_id = "nielsr/image-segmentation-toy-data"
image1 = Image.open(
hf_hub_download(repo_id=repo_id, filename="instance_segmentation_image_1.png", repo_type="dataset")
)
image2 = Image.open(
hf_hub_download(repo_id=repo_id, filename="instance_segmentation_image_2.png", repo_type="dataset")
)
annotation1 = Image.open(
hf_hub_download(repo_id=repo_id, filename="instance_segmentation_annotation_1.png", repo_type="dataset")
)
annotation2 = Image.open(
hf_hub_download(repo_id=repo_id, filename="instance_segmentation_annotation_2.png", repo_type="dataset")
)
# get instance segmentations and instance-to-segmentation mappings
def get_instance_segmentation_and_mapping(annotation):
instance_seg = np.array(annotation)[:, :, 1]
class_id_map = np.array(annotation)[:, :, 0]
class_labels = np.unique(class_id_map)
# create mapping between instance IDs and semantic category IDs
inst2class = {}
for label in class_labels:
instance_ids = np.unique(instance_seg[class_id_map == label])
inst2class.update({i: label for i in instance_ids})
return instance_seg, inst2class
instance_seg1, inst2class1 = get_instance_segmentation_and_mapping(annotation1)
instance_seg2, inst2class2 = get_instance_segmentation_and_mapping(annotation2)
# create a image processor
image_processing = MaskFormerImageProcessor(reduce_labels=True, ignore_index=255, size=(512, 512))
# prepare the images and annotations
inputs = image_processing(
[image1, image2],
[instance_seg1, instance_seg2],
instance_id_to_semantic_id=[inst2class1, inst2class2],
return_tensors="pt",
)
# verify the pixel values and pixel mask
self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 512))
self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 512))
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([30, 55])))
self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([4, 4, 23, 55])))
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)
self.assertEqual(inputs["mask_labels"][0].shape, (2, 512, 512))
self.assertEqual(inputs["mask_labels"][1].shape, (4, 512, 512))
self.assertEquals(inputs["mask_labels"][0].sum().item(), 41527.0)
self.assertEquals(inputs["mask_labels"][1].sum().item(), 26259.0)
def test_integration_semantic_segmentation(self):
# load 2 images and corresponding semantic annotations from the hub
repo_id = "nielsr/image-segmentation-toy-data"
image1 = Image.open(
hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_image_1.png", repo_type="dataset")
)
image2 = Image.open(
hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_image_2.png", repo_type="dataset")
)
annotation1 = Image.open(
hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_annotation_1.png", repo_type="dataset")
)
annotation2 = Image.open(
hf_hub_download(repo_id=repo_id, filename="semantic_segmentation_annotation_2.png", repo_type="dataset")
)
# create a image processor
image_processing = MaskFormerImageProcessor(reduce_labels=True, ignore_index=255, size=(512, 512))
# prepare the images and annotations
inputs = image_processing(
[image1, image2],
[annotation1, annotation2],
return_tensors="pt",
)
# verify the pixel values and pixel mask
self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 512))
self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 512))
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor([2, 4, 60])))
self.assertTrue(torch.allclose(inputs["class_labels"][1], torch.tensor([0, 3, 7, 8, 15, 28, 30, 143])))
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)
self.assertEqual(inputs["mask_labels"][0].shape, (3, 512, 512))
self.assertEqual(inputs["mask_labels"][1].shape, (8, 512, 512))
self.assertEquals(inputs["mask_labels"][0].sum().item(), 170200.0)
self.assertEquals(inputs["mask_labels"][1].sum().item(), 257036.0)
def test_integration_panoptic_segmentation(self):
# load 2 images and corresponding panoptic annotations from the hub
dataset = load_dataset("nielsr/ade20k-panoptic-demo")
image1 = dataset["train"][0]["image"]
image2 = dataset["train"][1]["image"]
segments_info1 = dataset["train"][0]["segments_info"]
segments_info2 = dataset["train"][1]["segments_info"]
annotation1 = dataset["train"][0]["label"]
annotation2 = dataset["train"][1]["label"]
def rgb_to_id(color):
if isinstance(color, np.ndarray) and len(color.shape) == 3:
if color.dtype == np.uint8:
color = color.astype(np.int32)
return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
def create_panoptic_map(annotation, segments_info):
annotation = np.array(annotation)
# convert RGB to segment IDs per pixel
# 0 is the "ignore" label, for which we don't need to make binary masks
panoptic_map = rgb_to_id(annotation)
# create mapping between segment IDs and semantic classes
inst2class = {segment["id"]: segment["category_id"] for segment in segments_info}
return panoptic_map, inst2class
panoptic_map1, inst2class1 = create_panoptic_map(annotation1, segments_info1)
panoptic_map2, inst2class2 = create_panoptic_map(annotation2, segments_info2)
# create a image processor
image_processing = MaskFormerImageProcessor(ignore_index=0, do_resize=False)
# prepare the images and annotations
pixel_values_list = [np.moveaxis(np.array(image1), -1, 0), np.moveaxis(np.array(image2), -1, 0)]
inputs = image_processing.encode_inputs(
pixel_values_list,
[panoptic_map1, panoptic_map2],
instance_id_to_semantic_id=[inst2class1, inst2class2],
return_tensors="pt",
)
# verify the pixel values and pixel mask
self.assertEqual(inputs["pixel_values"].shape, (2, 3, 512, 711))
self.assertEqual(inputs["pixel_mask"].shape, (2, 512, 711))
# verify the class labels
self.assertEqual(len(inputs["class_labels"]), 2)
# fmt: off
expected_class_labels = torch.tensor([4, 17, 32, 42, 42, 42, 42, 42, 42, 42, 32, 12, 12, 12, 12, 12, 42, 42, 12, 12, 12, 42, 12, 12, 12, 12, 12, 3, 12, 12, 12, 12, 42, 42, 42, 12, 42, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 5, 12, 12, 12, 12, 12, 12, 12, 0, 43, 43, 43, 96, 43, 104, 43, 31, 125, 31, 125, 138, 87, 125, 149, 138, 125, 87, 87]) # noqa: E231
# fmt: on
self.assertTrue(torch.allclose(inputs["class_labels"][0], torch.tensor(expected_class_labels)))
# fmt: off
expected_class_labels = torch.tensor([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 67, 82, 19, 19, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 12, 12, 42, 12, 12, 12, 12, 3, 14, 12, 12, 12, 12, 12, 12, 12, 12, 14, 5, 12, 12, 0, 115, 43, 43, 115, 43, 43, 43, 8, 8, 8, 138, 138, 125, 143]) # noqa: E231
# fmt: on
self.assertTrue(torch.allclose(inputs["class_labels"][1], expected_class_labels))
# verify the mask labels
self.assertEqual(len(inputs["mask_labels"]), 2)
self.assertEqual(inputs["mask_labels"][0].shape, (79, 512, 711))
self.assertEqual(inputs["mask_labels"][1].shape, (61, 512, 711))
self.assertEquals(inputs["mask_labels"][0].sum().item(), 315193.0)
self.assertEquals(inputs["mask_labels"][1].sum().item(), 350747.0)
def test_binary_mask_to_rle(self):
fake_binary_mask = np.zeros((20, 50))
fake_binary_mask[0, 20:] = 1
fake_binary_mask[1, :15] = 1
fake_binary_mask[5, :10] = 1
rle = binary_mask_to_rle(fake_binary_mask)
self.assertEqual(len(rle), 4)
self.assertEqual(rle[0], 21)
self.assertEqual(rle[1], 45)
def test_post_process_segmentation(self):
fature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = fature_extractor.post_process_segmentation(outputs)
self.assertEqual(
segmentation.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_classes,
self.image_processor_tester.height,
self.image_processor_tester.width,
),
)
target_size = (1, 4)
segmentation = fature_extractor.post_process_segmentation(outputs, target_size=target_size)
self.assertEqual(
segmentation.shape,
(self.image_processor_tester.batch_size, self.image_processor_tester.num_classes, *target_size),
)
def test_post_process_semantic_segmentation(self):
fature_extractor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = fature_extractor.post_process_semantic_segmentation(outputs)
self.assertEqual(len(segmentation), self.image_processor_tester.batch_size)
self.assertEqual(
segmentation[0].shape,
(
self.image_processor_tester.height,
self.image_processor_tester.width,
),
)
target_sizes = [(1, 4) for i in range(self.image_processor_tester.batch_size)]
segmentation = fature_extractor.post_process_semantic_segmentation(outputs, target_sizes=target_sizes)
self.assertEqual(segmentation[0].shape, target_sizes[0])
def test_post_process_instance_segmentation(self):
image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = image_processor.post_process_instance_segmentation(outputs, threshold=0)
self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
for el in segmentation:
self.assertTrue("segmentation" in el)
self.assertTrue("segments_info" in el)
self.assertEqual(type(el["segments_info"]), list)
self.assertEqual(
el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
)
segmentation = image_processor.post_process_instance_segmentation(
outputs, threshold=0, return_binary_maps=True
)
self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
for el in segmentation:
self.assertTrue("segmentation" in el)
self.assertTrue("segments_info" in el)
self.assertEqual(type(el["segments_info"]), list)
self.assertEqual(len(el["segmentation"].shape), 3)
self.assertEqual(
el["segmentation"].shape[1:], (self.image_processor_tester.height, self.image_processor_tester.width)
)
def test_post_process_panoptic_segmentation(self):
image_processing = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = image_processing.post_process_panoptic_segmentation(outputs, threshold=0)
self.assertTrue(len(segmentation) == self.image_processor_tester.batch_size)
for el in segmentation:
self.assertTrue("segmentation" in el)
self.assertTrue("segments_info" in el)
self.assertEqual(type(el["segments_info"]), list)
self.assertEqual(
el["segmentation"].shape, (self.image_processor_tester.height, self.image_processor_tester.width)
)
def test_post_process_label_fusing(self):
image_processor = self.image_processing_class(num_labels=self.image_processor_tester.num_classes)
outputs = self.image_processor_tester.get_fake_maskformer_outputs()
segmentation = image_processor.post_process_panoptic_segmentation(
outputs, threshold=0, mask_threshold=0, overlap_mask_area_threshold=0
)
unfused_segments = [el["segments_info"] for el in segmentation]
fused_segmentation = image_processor.post_process_panoptic_segmentation(
outputs, threshold=0, mask_threshold=0, overlap_mask_area_threshold=0, label_ids_to_fuse={1}
)
fused_segments = [el["segments_info"] for el in fused_segmentation]
for el_unfused, el_fused in zip(unfused_segments, fused_segments):
if len(el_unfused) == 0:
self.assertEqual(len(el_unfused), len(el_fused))
continue
# Get number of segments to be fused
fuse_targets = [1 for el in el_unfused if el["label_id"] in {1}]
num_to_fuse = 0 if len(fuse_targets) == 0 else sum(fuse_targets) - 1
# Expected number of segments after fusing
expected_num_segments = max([el["id"] for el in el_unfused]) - num_to_fuse
num_segments_fused = max([el["id"] for el in el_fused])
self.assertEqual(num_segments_fused, expected_num_segments)
| 27,734 | 43.661836 | 380 | py |
transformers | transformers-main/tests/models/xlm_roberta/test_modeling_xlm_roberta.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class XLMRobertaModelIntegrationTest(unittest.TestCase):
@slow
def test_xlm_roberta_base(self):
model = XLMRobertaModel.from_pretrained("xlm-roberta-base")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 768)) # batch_size, sequence_length, embedding_vector_dim
expected_output_values_last_dim = torch.tensor(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]
)
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
@slow
def test_xlm_roberta_large(self):
model = XLMRobertaModel.from_pretrained("xlm-roberta-large")
input_ids = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]])
# The dog is cute and lives in the garden house
expected_output_shape = torch.Size((1, 12, 1024)) # batch_size, sequence_length, embedding_vector_dim
expected_output_values_last_dim = torch.tensor(
[[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]
)
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
output = model(input_ids)["last_hidden_state"].detach()
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
| 3,127 | 43.685714 | 116 | py |
transformers | transformers-main/tests/models/xlm_roberta/test_modeling_flax_xlm_roberta.py | # coding=utf-8
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class FlaxXLMRobertaModelIntegrationTest(unittest.TestCase):
@slow
def test_flax_xlm_roberta_base(self):
model = FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base")
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
text = "The dog is cute and lives in the garden house"
input_ids = jnp.array([tokenizer.encode(text)])
expected_output_shape = (1, 12, 768) # batch_size, sequence_length, embedding_vector_dim
expected_output_values_last_dim = jnp.array(
[[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]
)
output = model(input_ids)["last_hidden_state"]
self.assertEqual(output.shape, expected_output_shape)
# compare the actual values for a slice of last dim
self.assertTrue(jnp.allclose(output[:, :, -1], expected_output_values_last_dim, atol=1e-3))
| 1,890 | 37.591837 | 113 | py |
transformers | transformers-main/tests/models/xlm_roberta/test_tokenization_xlm_roberta.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class XLMRobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = XLMRobertaTokenizer
rust_tokenizer_class = XLMRobertaTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<pad>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<s>")
self.assertEqual(vocab_keys[1], "<pad>")
self.assertEqual(vocab_keys[-1], "<mask>")
self.assertEqual(len(vocab_keys), 1_002)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_002)
def test_full_tokenizer(self):
tokenizer = XLMRobertaTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
# overwrite from test_tokenization_common to speed up test
def test_save_pretrained(self):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
self.tokenizers_list[0] = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
tokenizer_r_files = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f)
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=True
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=True)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it save with the same files
self.assertSequenceEqual(tokenizer_r_files, tokenizer_p_files)
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
# Save tokenizer rust, legacy_format=False
tmpdirname2 = tempfile.mkdtemp()
tokenizer_r_files = tokenizer_r.save_pretrained(tmpdirname2, legacy_format=False)
tokenizer_p_files = tokenizer_p.save_pretrained(tmpdirname2)
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files))
# Checks everything loads correctly in the same way
tokenizer_rp = tokenizer_r.from_pretrained(tmpdirname2)
tokenizer_pp = tokenizer_p.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(tokenizer_rp, key))
shutil.rmtree(tmpdirname2)
@cached_property
def big_tokenizer(self):
return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")
def test_picklable_without_disk(self):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SAMPLE_VOCAB, f.name)
tokenizer = XLMRobertaTokenizer(f.name, keep_accents=True)
pickled_tokenizer = pickle.dumps(tokenizer)
pickle.loads(pickled_tokenizer)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [0, 35378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
original_tokenizer_encodings = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
179459,
124850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
10114,
711,
152,
20,
6,
5,
22376,
642,
1221,
15190,
34153,
450,
5608,
959,
1119,
57702,
136,
186,
47,
1098,
29367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
50901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="xlm-roberta-base",
revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3",
)
| 14,689 | 41.456647 | 2,392 | py |
transformers | transformers-main/tests/models/yoso/test_modeling_yoso.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch YOSO model. """
import unittest
from transformers import YosoConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
YosoForMaskedLM,
YosoForMultipleChoice,
YosoForQuestionAnswering,
YosoForSequenceClassification,
YosoForTokenClassification,
YosoModel,
)
from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST
class YosoModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return YosoConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = YosoModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = YosoModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = YosoForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = YosoForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = YosoForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = YosoForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = YosoForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class YosoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
YosoModel,
YosoForMaskedLM,
YosoForMultipleChoice,
YosoForQuestionAnswering,
YosoForSequenceClassification,
YosoForTokenClassification,
)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
test_torchscript = False
all_generative_model_classes = ()
pipeline_model_mapping = (
{
"feature-extraction": YosoModel,
"fill-mask": YosoForMaskedLM,
"question-answering": YosoForQuestionAnswering,
"text-classification": YosoForSequenceClassification,
"token-classification": YosoForTokenClassification,
"zero-shot": YosoForSequenceClassification,
}
if is_torch_available()
else {}
)
def setUp(self):
self.model_tester = YosoModelTester(self)
self.config_tester = ConfigTester(self, config_class=YosoConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = YosoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_attention_outputs(self):
return
@require_torch
class YosoModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = YosoModel.from_pretrained("uw-madison/yoso-4096")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-0.0611, 0.1242, 0.0840], [0.0280, -0.0048, 0.1125], [0.0106, 0.0226, 0.0751]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_masked_lm(self):
model = YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 50265
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-2.1313, -3.7285, -2.2407], [-2.7047, -3.3314, -2.6408], [0.0629, -2.5166, -0.3356]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_masked_lm_long_input(self):
model = YosoForMaskedLM.from_pretrained("uw-madison/yoso-4096")
input_ids = torch.arange(4096).unsqueeze(0)
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 50265
expected_shape = torch.Size((1, 4096, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-2.3914, -4.3742, -5.0956], [-4.0988, -4.2384, -7.0406], [-3.1427, -3.7192, -6.6800]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| 15,779 | 36.751196 | 117 | py |
transformers | transformers-main/tests/models/vit_hybrid/test_modeling_vit_hybrid.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch ViT Hybrid model. """
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class ViTHybridModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
patch_size=2,
num_channels=3,
is_training=True,
use_labels=True,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
type_sequence_label_size=10,
initializer_range=0.02,
backbone_featmap_shape=[1, 16, 4, 4],
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.is_training = is_training
self.use_labels = use_labels
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.scope = scope
self.backbone_featmap_shape = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
num_patches = (self.image_size // 32) ** 2
self.seq_length = num_patches + 1
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
backbone_config = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [4, 8, 16, 32],
"num_groups": 2,
}
return ViTHybridConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
is_decoder=False,
initializer_range=self.initializer_range,
backbone_featmap_shape=self.backbone_featmap_shape,
backbone_config=backbone_config,
)
def create_and_check_model(self, config, pixel_values, labels):
model = ViTHybridModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.type_sequence_label_size
model = ViTHybridForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class ViTHybridModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as ViT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = ViTHybridModelTester(self)
self.config_tester = ConfigTester(self, config_class=ViTHybridConfig, has_text_modality=False, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@slow
def test_model_from_pretrained(self):
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ViTHybridModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class ViTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
@slow
def test_inference_image_classification_head(self):
model = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
torch_device
)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-1.9090, -0.4993, -0.2389]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
@require_accelerate
def test_accelerate_inference(self):
image_processor = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384")
model = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384", device_map="auto")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
self.assertTrue(model.config.id2label[predicted_class_idx], "tabby, tabby cat")
| 11,328 | 38.065517 | 118 | py |
transformers | transformers-main/tests/models/mobilevit/test_modeling_tf_mobilevit.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow MobileViT model. """
from __future__ import annotations
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class TFMobileViTConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
class TFMobileViTModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=640,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
output_stride=32,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
classifier_dropout_prob=0.1,
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=10,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.last_hidden_size = last_hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.conv_kernel_size = conv_kernel_size
self.output_stride = output_stride
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.classifier_dropout_prob = classifier_dropout_prob
self.use_labels = use_labels
self.is_training = is_training
self.num_labels = num_labels
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return MobileViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
conv_kernel_size=self.conv_kernel_size,
output_stride=self.output_stride,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = TFMobileViTModel(config=config)
result = model(pixel_values, training=False)
expected_height = expected_width = self.image_size // self.output_stride
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.last_hidden_size, expected_height, expected_width)
)
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = TFMobileViTForImageClassification(config)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = TFMobileViTForSemanticSegmentation(config)
expected_height = expected_width = self.image_size // self.output_stride
result = model(pixel_values, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
)
result = model(pixel_values, labels=pixel_labels, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFMobileViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
if is_tf_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
test_onnx = False
def setUp(self):
self.model_tester = TFMobileViTModelTester(self)
self.config_tester = TFMobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MobileViT does not output attentions")
def test_attention_outputs(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = 5
self.assertEqual(len(hidden_states), expected_num_stages)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
divisor = 2
for i in range(len(hidden_states)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
)
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_for_semantic_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
if model_class.__name__ != "TFMobileViTModel":
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
super().test_keras_fit()
# The default test_loss_computation() uses -100 as a proxy ignore_index
# to test masked losses. Overridding to avoid -100 since semantic segmentation
# models use `semantic_loss_ignore_index` from the config.
def test_loss_computation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# set an ignore index to correctly test the masked loss used in
# `TFMobileViTForSemanticSegmentation`.
if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
config.semantic_loss_ignore_index = 5
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
# The number of elements in the loss should be the same as the number of elements in the label
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
added_label = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]
]
expected_loss_size = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
loss = model(model_input, **prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss when we mask some positions
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
if "labels" in prepared_for_class:
labels = prepared_for_class["labels"].numpy()
if len(labels.shape) > 1 and labels.shape[1] != 1:
# labels[0] = -100
prepared_for_class["labels"] = tf.convert_to_tensor(labels)
loss = model(model_input, **prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
self.assertTrue(not np.any(np.isnan(loss.numpy())))
# Test that model correctly compute the loss with a dict
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
loss = model(prepared_for_class)[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
# Test that model correctly compute the loss with a tuple
prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
# Get keys that were added with the _prepare_for_class function
label_keys = prepared_for_class.keys() - inputs_dict.keys()
signature = inspect.signature(model.call).parameters
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
# Initialize a list with their default values, update the values and convert to a tuple
list_input = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default)
for index, value in sorted_tuple_index_mapping:
list_input[index] = prepared_for_class[value]
tuple_input = tuple(list_input)
# Send to model
loss = model(tuple_input[:-1])[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1])
@slow
def test_model_from_pretrained(self):
for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFMobileViTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
class TFMobileViTModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_classification_head(self):
model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small")
image_processor = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(**inputs, training=False)
# verify the logits
expected_shape = tf.TensorShape((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant([-1.9364, -1.2327, -0.4653])
tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4, rtol=1e-04)
@slow
def test_inference_semantic_segmentation(self):
# `from_pt` will be removed
model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="tf")
# forward pass
outputs = model(inputs.pixel_values, training=False)
logits = outputs.logits
# verify the logits
expected_shape = tf.TensorShape((1, 21, 32, 32))
self.assertEqual(logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
]
)
tf.debugging.assert_near(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
| 18,355 | 42.089202 | 124 | py |
transformers | transformers-main/tests/models/mobilevit/test_image_processing_mobilevit.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class MobileViTImageProcessingTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
num_channels=3,
image_size=18,
min_resolution=30,
max_resolution=400,
do_resize=True,
size=None,
do_center_crop=True,
crop_size=None,
do_flip_channel_order=True,
):
size = size if size is not None else {"shortest_edge": 20}
crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
self.parent = parent
self.batch_size = batch_size
self.num_channels = num_channels
self.image_size = image_size
self.min_resolution = min_resolution
self.max_resolution = max_resolution
self.do_resize = do_resize
self.size = size
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_flip_channel_order = do_flip_channel_order
def prepare_image_processor_dict(self):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class MobileViTImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = MobileViTImageProcessor if is_vision_available() else None
def setUp(self):
self.image_processor_tester = MobileViTImageProcessingTester(self)
@property
def image_processor_dict(self):
return self.image_processor_tester.prepare_image_processor_dict()
def test_image_processor_properties(self):
image_processing = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(image_processing, "do_resize"))
self.assertTrue(hasattr(image_processing, "size"))
self.assertTrue(hasattr(image_processing, "do_center_crop"))
self.assertTrue(hasattr(image_processing, "center_crop"))
self.assertTrue(hasattr(image_processing, "do_flip_channel_order"))
def test_image_processor_from_dict_with_kwargs(self):
image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 20})
self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
self.assertEqual(image_processor.size, {"shortest_edge": 42})
self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
def test_batch_feature(self):
pass
def test_call_pil(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False)
for image in image_inputs:
self.assertIsInstance(image, Image.Image)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_numpy(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True)
for image in image_inputs:
self.assertIsInstance(image, np.ndarray)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
def test_call_pytorch(self):
# Initialize image_processing
image_processing = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True)
for image in image_inputs:
self.assertIsInstance(image, torch.Tensor)
# Test not batched input
encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
# Test batched
encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
self.assertEqual(
encoded_images.shape,
(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
),
)
| 7,619 | 36.722772 | 113 | py |
transformers | transformers-main/tests/models/mobilevit/test_modeling_mobilevit.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch MobileViT model. """
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class MobileViTConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "neck_hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
class MobileViTModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=32,
patch_size=2,
num_channels=3,
last_hidden_size=640,
num_attention_heads=4,
hidden_act="silu",
conv_kernel_size=3,
output_stride=32,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
classifier_dropout_prob=0.1,
initializer_range=0.02,
is_training=True,
use_labels=True,
num_labels=10,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.last_hidden_size = last_hidden_size
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.conv_kernel_size = conv_kernel_size
self.output_stride = output_stride
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.classifier_dropout_prob = classifier_dropout_prob
self.use_labels = use_labels
self.is_training = is_training
self.num_labels = num_labels
self.initializer_range = initializer_range
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
pixel_labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels, pixel_labels
def get_config(self):
return MobileViTConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
conv_kernel_size=self.conv_kernel_size,
output_stride=self.output_stride,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
classifier_dropout_prob=self.classifier_dropout_prob,
initializer_range=self.initializer_range,
)
def create_and_check_model(self, config, pixel_values, labels, pixel_labels):
model = MobileViTModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels):
config.num_labels = self.num_labels
model = MobileViTForSemanticSegmentation(config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
result = model(pixel_values, labels=pixel_labels)
self.parent.assertEqual(
result.logits.shape,
(
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
),
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels, pixel_labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = MobileViTModelTester(self)
self.config_tester = MobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings")
def test_model_common_attributes(self):
pass
@unittest.skip(reason="MobileViT does not output attentions")
def test_attention_outputs(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
def test_model_is_small(self):
pass
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_stages = 5
self.assertEqual(len(hidden_states), expected_num_stages)
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
divisor = 2
for i in range(len(hidden_states)):
self.assertListEqual(
list(hidden_states[i].shape[-2:]),
[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor],
)
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_for_semantic_segmentation(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = MobileViTModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class MobileViTModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-1.9364, -1.2327, -0.4653]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@slow
def test_inference_semantic_segmentation(self):
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
# verify the logits
expected_shape = torch.Size((1, 21, 32, 32))
self.assertEqual(logits.shape, expected_shape)
expected_slice = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
],
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
@slow
def test_post_processing_semantic_segmentation(self):
model = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
model = model.to(torch_device)
image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small")
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
outputs.logits = outputs.logits.detach().cpu()
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)])
expected_shape = torch.Size((50, 60))
self.assertEqual(segmentation[0].shape, expected_shape)
segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs)
expected_shape = torch.Size((32, 32))
self.assertEqual(segmentation[0].shape, expected_shape)
| 14,406 | 36.813648 | 124 | py |
transformers | transformers-main/tests/models/pegasus/test_modeling_tf_pegasus.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeq2SeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class TFPegasusModelTester:
config_cls = PegasusConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=40,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size)
eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1)
input_ids = tf.concat([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = TFPegasusModel(config=config).get_decoder()
input_ids = inputs_dict["input_ids"]
input_ids = input_ids[:1, :]
attention_mask = inputs_dict["attention_mask"][:1, :]
head_mask = inputs_dict["head_mask"]
self.batch_size = 1
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8)
# append to next input_ids and
next_input_ids = tf.concat([input_ids, next_tokens], axis=-1)
next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1])
# select random slice
random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1]))
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx]
output_from_past_slice = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3)
def prepare_pegasus_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8)
if decoder_attention_mask is None:
decoder_attention_mask = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8),
],
axis=-1,
)
if head_mask is None:
head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads))
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class TFPegasusModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
all_generative_model_classes = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
pipeline_model_mapping = (
{
"conversational": TFPegasusForConditionalGeneration,
"feature-extraction": TFPegasusModel,
"summarization": TFPegasusForConditionalGeneration,
"text2text-generation": TFPegasusForConditionalGeneration,
"translation": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
is_encoder_decoder = True
test_pruning = False
test_onnx = False
def setUp(self):
self.model_tester = TFPegasusModelTester(self)
self.config_tester = ConfigTester(self, config_class=PegasusConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs)
@require_sentencepiece
@require_tokenizers
@require_tf
class TFPegasusIntegrationTests(unittest.TestCase):
src_text = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" """,
]
expected_text = [
"California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
" reduce the risk of wildfires.",
'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
model_name = "google/pegasus-xsum"
@cached_property
def tokenizer(self):
return AutoTokenizer.from_pretrained(self.model_name)
@cached_property
def model(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name)
return model
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
generated_words = self.translate_src_text(**tokenizer_kwargs)
assert self.expected_text == generated_words
def translate_src_text(self, **tokenizer_kwargs):
model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, padding=True, return_tensors="tf")
generated_ids = self.model.generate(
model_inputs.input_ids,
attention_mask=model_inputs.attention_mask,
num_beams=2,
use_cache=True,
)
generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)
return generated_words
@slow
def test_batch_generation(self):
self._assert_generated_batch_equal_expected()
| 11,864 | 46.083333 | 1,802 | py |
transformers | transformers-main/tests/models/pegasus/test_modeling_flax_pegasus.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform"
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class FlaxPegasusModelTester:
config_cls = PegasusConfig
config_updates = {}
hidden_act = "gelu"
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs_for_common(self):
input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size).clip(3, self.vocab_size)
eos_tensor = np.expand_dims(np.array([self.eos_token_id] * self.batch_size), 1)
input_ids = np.concatenate([input_ids, eos_tensor], axis=1)
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.config_cls(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_ids=[2],
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.pad_token_id,
**self.config_updates,
)
inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4")
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
past_key_values=outputs_cache.past_key_values,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict):
max_decoder_length = 20
model = model_class_name(config)
encoder_outputs = model.encode(inputs_dict["input_ids"])
decoder_input_ids, decoder_attention_mask = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
decoder_attention_mask_cache = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
],
axis=-1,
)
past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :],
(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),
)
outputs_cache = model.decode(
decoder_input_ids[:, :-1],
encoder_outputs,
decoder_attention_mask=decoder_attention_mask_cache,
past_key_values=past_key_values,
decoder_position_ids=decoder_position_ids,
)
decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model.decode(
decoder_input_ids[:, -1:],
encoder_outputs,
past_key_values=outputs_cache.past_key_values,
decoder_attention_mask=decoder_attention_mask_cache,
decoder_position_ids=decoder_position_ids,
)
outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def prepare_pegasus_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
):
if attention_mask is None:
attention_mask = np.not_equal(input_ids, config.pad_token_id).astype(np.int8)
if decoder_attention_mask is None:
decoder_attention_mask = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape, dtype=np.int8),
np.not_equal(decoder_input_ids[:, 1:], config.pad_token_id).astype(np.int8),
],
axis=-1,
)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class FlaxPegasusModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
all_generative_model_classes = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
is_encoder_decoder = True
test_pruning = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = FlaxPegasusModelTester(self)
self.config_tester = ConfigTester(self, config_class=PegasusConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_use_cache_forward(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(model_class, config, inputs_dict)
def test_use_cache_forward_with_attn_mask(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict)
def test_encode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def encode_jitted(input_ids, attention_mask=None, **kwargs):
return model.encode(input_ids=input_ids, attention_mask=attention_mask)
with self.subTest("JIT Enabled"):
jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = encode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_decode(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
model = model_class(config)
encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"])
prepared_inputs_dict = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs):
return model.decode(
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
)
with self.subTest("JIT Enabled"):
jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = decode_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("google/pegasus-large", from_pt=True)
input_ids = np.ones((1, 1))
outputs = model(input_ids)
self.assertIsNotNone(outputs)
@slow
def test_pegasus_xsum_summary(self):
model = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum")
tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-xsum")
src_text = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" """,
]
tgt_text = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
inputs = tokenizer(src_text, return_tensors="np", truncation=True, max_length=512, padding=True)
translated_tokens = model.generate(**inputs, num_beams=2).sequences
decoded = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert tgt_text == decoded
| 15,723 | 45.383481 | 1,806 | py |
transformers | transformers-main/tests/models/pegasus/test_tokenization_pegasus.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = PegasusTokenizer
rust_tokenizer_class = PegasusTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = PegasusTokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _large_tokenizer(self):
return PegasusTokenizer.from_pretrained("google/pegasus-large")
def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
return ("This is a test", "This is a test")
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "</s>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<pad>")
self.assertEqual(vocab_keys[1], "</s>")
self.assertEqual(vocab_keys[-1], "v")
self.assertEqual(len(vocab_keys), 1_103)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_103)
def test_mask_tokens_rust_pegasus(self):
rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
raw_input_str = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
self.assertListEqual(py_ids, rust_ids)
def test_large_mask_tokens(self):
tokenizer = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
raw_input_str = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
desired_result = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
self.assertListEqual(desired_result, ids)
def test_large_tokenizer_settings(self):
tokenizer = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
raw_input_str = "To ensure a smooth flow of bank resolutions."
desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0]
self.assertListEqual(desired_result, ids)
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def test_large_seq2seq_truncation(self):
src_texts = ["This is going to be way too long." * 150, "short example"]
tgt_texts = ["not super long but more than 5 tokens", "tiny"]
batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt")
targets = self._large_tokenizer(
text_target=tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt"
)
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(batch) == 2 # input_ids, attention_mask.
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/bigbird-pegasus-large-arxiv",
revision="ba85d0851d708441f91440d509690f1ab6353415",
)
@require_sentencepiece
@require_tokenizers
class BigBirdPegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = PegasusTokenizer
rust_tokenizer_class = PegasusTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
# We have a SentencePiece fixture for testing
tokenizer = PegasusTokenizer(SAMPLE_VOCAB, offset=0, mask_token_sent=None, mask_token="[MASK]")
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def _large_tokenizer(self):
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
def get_tokenizer(self, **kwargs) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_input_output_texts(self, tokenizer):
return ("This is a test", "This is a test")
def test_mask_tokens_rust_pegasus(self):
rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname)
raw_input_str = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0]
self.assertListEqual(py_ids, rust_ids)
@require_torch
def test_large_seq2seq_truncation(self):
src_texts = ["This is going to be way too long." * 1000, "short example"]
tgt_texts = ["not super long but more than 5 tokens", "tiny"]
batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt")
targets = self._large_tokenizer(
text_target=tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt"
)
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(batch) == 2 # input_ids, attention_mask.
def test_equivalence_to_orig_tokenizer(self):
"""
To run with original TF tokenizer:
!wget https://github.com/google-research/bigbird/raw/master/bigbird/vocab/pegasus.model
!pip install tensorflow-text
import tensorflow.compat.v2 as tf
import tensorflow_text as tft
VOCAB_FILE = "./pegasus.model"
tf.enable_v2_behavior()
test_str = "This is an example string that is used to test the original TF implementation against the HF implementation"
tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(VOCAB_FILE, "rb").read())
tokenizer.tokenize(test_str)
"""
test_str = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
token_ids = self._large_tokenizer(test_str).input_ids
self.assertListEqual(
token_ids,
[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1],
)
| 10,824 | 49.584112 | 1,929 | py |
transformers | transformers-main/tests/models/pegasus/test_modeling_pegasus.py | # coding=utf-8
# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch PEGASUS model. """
import tempfile
import unittest
from transformers import PegasusConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
from ..mbart.test_modeling_mbart import AbstractSeq2SeqIntegrationTest
if is_torch_available():
import torch
from transformers import AutoModelForSeq2SeqLM, PegasusForConditionalGeneration, PegasusModel
from transformers.models.pegasus.modeling_pegasus import PegasusDecoder, PegasusEncoder, PegasusForCausalLM
def prepare_pegasus_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class PegasusModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
# forcing a certain token to be generated, sets all other tokens to -inf
# if however the token to be generated is already at -inf then it can lead token
# `nan` values and thus break generation
self.forced_bos_token_id = None
self.forced_eos_token_id = None
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
3,
)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
config = self.get_config()
inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_pipeline_config(self):
return PegasusConfig(
vocab_size=200,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=200,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def get_config(self):
return PegasusConfig(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
forced_bos_token_id=self.forced_bos_token_id,
forced_eos_token_id=self.forced_eos_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = PegasusModel(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = PegasusModel(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = PegasusEncoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = PegasusDecoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class PegasusModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (PegasusModel, PegasusForConditionalGeneration) if is_torch_available() else ()
all_generative_model_classes = (PegasusForConditionalGeneration,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"conversational": PegasusForConditionalGeneration,
"feature-extraction": PegasusModel,
"summarization": PegasusForConditionalGeneration,
"text-generation": PegasusForCausalLM,
"text2text-generation": PegasusForConditionalGeneration,
"translation": PegasusForConditionalGeneration,
}
if is_torch_available()
else {}
)
is_encoder_decoder = True
fx_compatible = True
test_resize_position_embeddings = True
test_pruning = False
test_missing_keys = False
def setUp(self):
self.model_tester = PegasusModelTester(self)
self.config_tester = ConfigTester(self, config_class=PegasusConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = PegasusForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def assert_tensors_close(a, b, atol=1e-12, prefix=""):
"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
if a is None and b is None:
return True
try:
if torch.allclose(a, b, atol=atol):
return True
raise
except Exception:
pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
if a.numel() > 100:
msg = f"tensor values are {pct_different:.1%} percent different."
else:
msg = f"{a} != {b}"
if prefix:
msg = prefix + ": " + msg
raise AssertionError(msg)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
@require_torch
@require_sentencepiece
@require_tokenizers
class PegasusXSUMIntegrationTest(AbstractSeq2SeqIntegrationTest):
checkpoint_name = "google/pegasus-xsum"
src_text = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning 'Oh I think you're nominated'", said Dappy."And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around."At the end of the day we're grateful to be where we are in our careers."If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" """,
]
tgt_text = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
@cached_property
def model(self):
return AutoModelForSeq2SeqLM.from_pretrained(self.checkpoint_name).to(torch_device)
@slow
def test_pegasus_xsum_summary(self):
assert self.tokenizer.model_max_length == 512
inputs = self.tokenizer(self.src_text, return_tensors="pt", truncation=True, max_length=512, padding=True).to(
torch_device
)
assert inputs.input_ids.shape == (2, 421)
translated_tokens = self.model.generate(**inputs, num_beams=2)
decoded = self.tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)
assert self.tgt_text == decoded
if "cuda" not in torch_device:
return
# Demonstrate fp16 issue, Contributions welcome!
self.model.half()
translated_tokens_fp16 = self.model.generate(**inputs, max_length=10)
decoded_fp16 = self.tokenizer.batch_decode(translated_tokens_fp16, skip_special_tokens=True)
assert decoded_fp16 == [
"California's largest electricity provider has begun",
"N-Dubz have revealed they were",
]
class PegasusStandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
encoder_attention_heads=4,
decoder_attention_heads=4,
max_position_embeddings=30,
is_encoder_decoder=False,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.is_encoder_decoder = is_encoder_decoder
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = PegasusConfig(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
encoder_attention_heads=self.encoder_attention_heads,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
is_encoder_decoder=self.is_encoder_decoder,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = PegasusDecoder(config=config).to(torch_device).eval()
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def create_and_check_decoder_model_attention_mask_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
model = PegasusDecoder(config=config).to(torch_device).eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = input_ids.shape[-1] // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=attn_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class PegasusStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (PegasusDecoder, PegasusForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (PegasusForCausalLM,) if is_torch_available() else ()
test_resize_position_embeddings = True
test_pruning = False
is_encoder_decoder = False
def setUp(
self,
):
self.model_tester = PegasusStandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=PegasusConfig)
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
def test_decoder_model_attn_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs)
def test_retain_grad_hidden_states_attentions(self):
# decoder cannot keep gradients
return
@unittest.skip("The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass
| 25,323 | 42.662069 | 1,802 | py |
transformers | transformers-main/tests/models/bit/test_modeling_bit.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Bit model. """
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class BitModelTester:
def __init__(
self,
parent,
batch_size=3,
image_size=32,
num_channels=3,
embeddings_size=10,
hidden_sizes=[8, 16, 32, 64],
depths=[1, 1, 2, 1],
is_training=True,
use_labels=True,
hidden_act="relu",
num_labels=3,
scope=None,
out_features=["stage2", "stage3", "stage4"],
out_indices=[2, 3, 4],
num_groups=1,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.embeddings_size = embeddings_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.num_labels = num_labels
self.scope = scope
self.num_stages = len(hidden_sizes)
self.out_features = out_features
self.out_indices = out_indices
self.num_groups = num_groups
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return BitConfig(
num_channels=self.num_channels,
embeddings_size=self.embeddings_size,
hidden_sizes=self.hidden_sizes,
depths=self.depths,
hidden_act=self.hidden_act,
num_labels=self.num_labels,
out_features=self.out_features,
out_indices=self.out_indices,
num_groups=self.num_groups,
)
def create_and_check_model(self, config, pixel_values, labels):
model = BitModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = BitForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = BitBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
self.parent.assertListEqual(model.channels, config.hidden_sizes[1:])
# verify backbone works with out_features=None
config.out_features = None
model = BitBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]])
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class BitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as Bit does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
pipeline_model_mapping = (
{"feature-extraction": BitModel, "image-classification": BitForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
has_attentions = False
def setUp(self):
self.model_tester = BitModelTester(self)
self.config_tester = ConfigTester(self, config_class=BitConfig, has_text_modality=False)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
@unittest.skip(reason="Bit does not output attentions")
def test_attention_outputs(self):
pass
@unittest.skip(reason="Bit does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Bit does not support input and output embeddings")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config=config)
for name, module in model.named_modules():
if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
self.assertTrue(
torch.all(module.weight == 1),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
self.assertTrue(
torch.all(module.bias == 0),
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
expected_num_stages = self.model_tester.num_stages
self.assertEqual(len(hidden_states), expected_num_stages + 1)
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.image_size // 4, self.model_tester.image_size // 4],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
layers_type = ["preactivation", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
config.layer_type = layer_type
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
@unittest.skip(reason="Bit does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BitModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_torch
@require_vision
class BitModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
)
@slow
def test_inference_image_classification_head(self):
model = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@require_torch
class BitBackboneTest(BackboneTesterMixin, unittest.TestCase):
all_model_classes = (BitBackbone,) if is_torch_available() else ()
config_class = BitConfig
has_attentions = False
def setUp(self):
self.model_tester = BitModelTester(self)
| 12,592 | 36.591045 | 119 | py |
transformers | transformers-main/tests/models/big_bird/test_modeling_big_bird.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch BigBird model. """
import unittest
from transformers import BigBirdConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.models.big_bird.tokenization_big_bird import BigBirdTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
BigBirdForCausalLM,
BigBirdForMaskedLM,
BigBirdForMultipleChoice,
BigBirdForPreTraining,
BigBirdForQuestionAnswering,
BigBirdForSequenceClassification,
BigBirdForTokenClassification,
BigBirdModel,
)
from transformers.models.big_bird.modeling_big_bird import BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST
class BigBirdModelTester:
def __init__(
self,
parent,
batch_size=7,
seq_length=128,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=256,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
attention_type="block_sparse",
use_bias=True,
rescale_embeddings=False,
block_size=8,
num_rand_blocks=3,
position_embedding_type="absolute",
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.attention_type = attention_type
self.use_bias = use_bias
self.rescale_embeddings = rescale_embeddings
self.block_size = block_size
self.num_rand_blocks = num_rand_blocks
self.position_embedding_type = position_embedding_type
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
return BigBirdConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_encoder_decoder=False,
initializer_range=self.initializer_range,
attention_type=self.attention_type,
use_bias=self.use_bias,
rescale_embeddings=self.rescale_embeddings,
block_size=self.block_size,
num_random_blocks=self.num_rand_blocks,
position_embedding_type=self.position_embedding_type,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BigBirdModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BigBirdForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, config.num_labels))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = BigBirdModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = BigBirdForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BigBirdForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = BigBirdForCausalLM(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BigBirdForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = BigBirdForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = BigBirdForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = BigBirdForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def create_and_check_for_auto_padding(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = BigBirdModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_change_to_full_attn(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
):
model = BigBirdModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
# the config should not be changed
self.parent.assertTrue(model.config.attention_type == "block_sparse")
@require_torch
class BigBirdModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
# head masking & pruning is currently not supported for big bird
test_head_masking = False
test_pruning = False
# torchscript should be possible, but takes prohibitively long to test.
# Also torchscript is not an important feature to have in the beginning.
test_torchscript = False
all_model_classes = (
(
BigBirdModel,
BigBirdForPreTraining,
BigBirdForMaskedLM,
BigBirdForCausalLM,
BigBirdForMultipleChoice,
BigBirdForQuestionAnswering,
BigBirdForSequenceClassification,
BigBirdForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (BigBirdForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": BigBirdModel,
"fill-mask": BigBirdForMaskedLM,
"question-answering": BigBirdForQuestionAnswering,
"text-classification": BigBirdForSequenceClassification,
"text-generation": BigBirdForCausalLM,
"token-classification": BigBirdForTokenClassification,
"zero-shot": BigBirdForSequenceClassification,
}
if is_torch_available()
else {}
)
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = BigBirdModelTester(self)
self.config_tester = ConfigTester(self, config_class=BigBirdConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_retain_grad_hidden_states_attentions(self):
# bigbird cannot keep gradients in attentions when `attention_type=block_sparse`
if self.model_tester.attention_type == "original_full":
super().test_retain_grad_hidden_states_attentions()
@slow
def test_model_from_pretrained(self):
for model_name in BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BigBirdForPreTraining.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_various_attn_type(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["original_full", "block_sparse"]:
config_and_inputs[0].attention_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_fast_integration(self):
# fmt: off
input_ids = torch.tensor(
[[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 122, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 44, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 98, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73],[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 12, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 28, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 18, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73]], # noqa: E231
dtype=torch.long,
device=torch_device,
)
# fmt: on
input_ids = input_ids % self.model_tester.vocab_size
input_ids[1] = input_ids[1] - 1
attention_mask = torch.ones((input_ids.shape), device=torch_device)
attention_mask[:, :-10] = 0
config, _, _, _, _, _, _ = self.model_tester.prepare_config_and_inputs()
torch.manual_seed(0)
model = BigBirdModel(config).eval().to(torch_device)
with torch.no_grad():
hidden_states = model(input_ids, attention_mask=attention_mask).last_hidden_state
self.assertTrue(
torch.allclose(
hidden_states[0, 0, :5],
torch.tensor([1.4825, 0.0774, 0.8226, -0.2962, -0.9593], device=torch_device),
atol=1e-3,
)
)
def test_auto_padding(self):
self.model_tester.seq_length = 241
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_auto_padding(*config_and_inputs)
def test_for_change_to_full_attn(self):
self.model_tester.seq_length = 9
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_change_to_full_attn(*config_and_inputs)
# overwrite from common in order to skip the check on `attentions`
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith("outputs.attentions"):
return
else:
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
@require_torch
@slow
class BigBirdModelIntegrationTest(unittest.TestCase):
# we can have this true once block_sparse attn_probs works accurately
test_attention_probs = False
def _get_dummy_input_ids(self):
# fmt: off
ids = torch.tensor(
[[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43, 64, 8, 53, 100, 92, 51, 122, 66, 17, 61, 50, 104, 127, 26, 35, 94, 23, 110, 71, 80, 67, 109, 111, 44, 19, 51, 41, 86, 71, 76, 44, 18, 68, 44, 77, 107, 81, 98, 126, 100, 2, 49, 98, 84, 39, 23, 98, 52, 46, 10, 82, 121, 73]], # noqa: E231
dtype=torch.long,
device=torch_device,
)
# fmt: on
return ids
def test_inference_block_sparse_pretraining(self):
model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base", attention_type="block_sparse")
model.to(torch_device)
input_ids = torch.tensor([[20920, 232, 328, 1437] * 1024], dtype=torch.long, device=torch_device)
with torch.no_grad():
outputs = model(input_ids)
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits
self.assertEqual(prediction_logits.shape, torch.Size((1, 4096, 50358)))
self.assertEqual(seq_relationship_logits.shape, torch.Size((1, 2)))
expected_prediction_logits_slice = torch.tensor(
[
[-0.5583, 0.0475, -0.2508, 7.4423],
[0.7409, 1.4460, -0.7593, 7.7010],
[1.9150, 3.1395, 5.8840, 9.3498],
[-0.1854, -1.4640, -2.2052, 3.7968],
],
device=torch_device,
)
self.assertTrue(
torch.allclose(prediction_logits[0, 128:132, 128:132], expected_prediction_logits_slice, atol=1e-4)
)
expected_seq_relationship_logits = torch.tensor([[46.9465, 47.9517]], device=torch_device)
self.assertTrue(torch.allclose(seq_relationship_logits, expected_seq_relationship_logits, atol=1e-4))
def test_inference_full_pretraining(self):
model = BigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base", attention_type="original_full")
model.to(torch_device)
input_ids = torch.tensor([[20920, 232, 328, 1437] * 512], dtype=torch.long, device=torch_device)
with torch.no_grad():
outputs = model(input_ids)
prediction_logits = outputs.prediction_logits
seq_relationship_logits = outputs.seq_relationship_logits
self.assertEqual(prediction_logits.shape, torch.Size((1, 512 * 4, 50358)))
self.assertEqual(seq_relationship_logits.shape, torch.Size((1, 2)))
expected_prediction_logits_slice = torch.tensor(
[
[0.1499, -1.1217, 0.1990, 8.4499],
[-2.7757, -3.0687, -4.8577, 7.5156],
[1.5446, 0.1982, 4.3016, 10.4281],
[-1.3705, -4.0130, -3.9629, 5.1526],
],
device=torch_device,
)
self.assertTrue(
torch.allclose(prediction_logits[0, 128:132, 128:132], expected_prediction_logits_slice, atol=1e-4)
)
expected_seq_relationship_logits = torch.tensor([[41.4503, 41.2406]], device=torch_device)
self.assertTrue(torch.allclose(seq_relationship_logits, expected_seq_relationship_logits, atol=1e-4))
def test_block_sparse_attention_probs(self):
"""
Asserting if outputted attention matrix is similar to hard coded attention matrix
"""
if not self.test_attention_probs:
return
model = BigBirdModel.from_pretrained(
"google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16
)
model.to(torch_device)
model.eval()
config = model.config
input_ids = self._get_dummy_input_ids()
hidden_states = model.embeddings(input_ids)
batch_size, seqlen, _ = hidden_states.size()
attn_mask = torch.ones(batch_size, seqlen, device=torch_device, dtype=torch.float)
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = config.block_size
blocked_mask, band_mask, from_mask, to_mask = model.create_masks_for_block_sparse_attn(
attn_mask, config.block_size
)
from_blocked_mask = to_blocked_mask = blocked_mask
for i in range(config.num_hidden_layers):
pointer = model.encoder.layer[i].attention.self
query_layer = pointer.transpose_for_scores(pointer.query(hidden_states))
key_layer = pointer.transpose_for_scores(pointer.key(hidden_states))
value_layer = pointer.transpose_for_scores(pointer.value(hidden_states))
context_layer, attention_probs = pointer.bigbird_block_sparse_attention(
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
pointer.num_attention_heads,
pointer.num_random_blocks,
pointer.attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_length,
to_seq_length,
seed=pointer.seed,
plan_from_length=None,
plan_num_rand_blocks=None,
output_attentions=True,
)
context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
cl = torch.einsum("bhqk,bhkd->bhqd", attention_probs, value_layer)
cl = cl.view(context_layer.size())
self.assertTrue(torch.allclose(context_layer, cl, atol=0.001))
def test_block_sparse_context_layer(self):
model = BigBirdModel.from_pretrained(
"google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16
)
model.to(torch_device)
model.eval()
config = model.config
input_ids = self._get_dummy_input_ids()
dummy_hidden_states = model.embeddings(input_ids)
attn_mask = torch.ones_like(input_ids, device=torch_device)
blocked_mask, band_mask, from_mask, to_mask = model.create_masks_for_block_sparse_attn(
attn_mask, config.block_size
)
targeted_cl = torch.tensor(
[
[0.1870, 1.5248, 0.2333, -0.0483, -0.0952, 1.8359, -0.0142, 0.1239, 0.0083, -0.0045],
[-0.0601, 0.1243, 0.1329, -0.1524, 0.2347, 0.0894, -0.2248, -0.2461, -0.0645, -0.0109],
[-0.0418, 0.1463, 0.1290, -0.1638, 0.2489, 0.0799, -0.2341, -0.2406, -0.0524, 0.0106],
[0.1859, 1.5182, 0.2324, -0.0473, -0.0952, 1.8295, -0.0148, 0.1242, 0.0080, -0.0045],
[0.1879, 1.5300, 0.2334, -0.0480, -0.0967, 1.8428, -0.0137, 0.1256, 0.0087, -0.0050],
[0.1852, 1.5149, 0.2330, -0.0492, -0.0936, 1.8236, -0.0154, 0.1210, 0.0080, -0.0048],
[0.1857, 1.5186, 0.2331, -0.0484, -0.0940, 1.8285, -0.0148, 0.1224, 0.0077, -0.0045],
[0.1884, 1.5336, 0.2334, -0.0469, -0.0974, 1.8477, -0.0132, 0.1266, 0.0085, -0.0046],
[0.1881, 1.5308, 0.2334, -0.0479, -0.0969, 1.8438, -0.0136, 0.1258, 0.0088, -0.0050],
[0.1849, 1.5143, 0.2329, -0.0491, -0.0930, 1.8230, -0.0156, 0.1209, 0.0074, -0.0047],
[0.1878, 1.5299, 0.2333, -0.0472, -0.0967, 1.8434, -0.0137, 0.1257, 0.0084, -0.0048],
[0.1873, 1.5260, 0.2333, -0.0478, -0.0961, 1.8383, -0.0142, 0.1245, 0.0083, -0.0048],
[0.1849, 1.5145, 0.2327, -0.0491, -0.0935, 1.8237, -0.0156, 0.1215, 0.0083, -0.0046],
[0.1866, 1.5232, 0.2332, -0.0488, -0.0950, 1.8342, -0.0143, 0.1237, 0.0084, -0.0047],
],
device=torch_device,
)
context_layer = model.encoder.layer[0].attention.self(
dummy_hidden_states,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=blocked_mask,
to_blocked_mask=blocked_mask,
)
context_layer = context_layer[0]
self.assertEqual(context_layer.shape, torch.Size((1, 128, 768)))
self.assertTrue(torch.allclose(context_layer[0, 64:78, 300:310], targeted_cl, atol=0.0001))
def test_tokenizer_inference(self):
tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdModel.from_pretrained(
"google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16
)
model.to(torch_device)
text = [
"Transformer-based models are unable to process long sequences due to their self-attention operation,"
" which scales quadratically with the sequence length. To address this limitation, we introduce the"
" Longformer with an attention mechanism that scales linearly with sequence length, making it easy to"
" process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in"
" replacement for the standard self-attention and combines a local windowed attention with a task"
" motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer"
" on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In"
" contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream"
" tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new"
" state-of-the-art results on WikiHop and TriviaQA."
]
inputs = tokenizer(text)
for k in inputs:
inputs[k] = torch.tensor(inputs[k], device=torch_device, dtype=torch.long)
prediction = model(**inputs)
prediction = prediction[0]
self.assertEqual(prediction.shape, torch.Size((1, 199, 768)))
expected_prediction = torch.tensor(
[
[0.1887, -0.0474, 0.2604, 0.1453],
[0.0651, 0.1999, 0.1797, 0.1161],
[0.2833, -0.3036, 0.6910, 0.1123],
[0.2836, -0.4644, -0.0111, 0.1530],
[0.3919, -0.2823, 0.4192, 0.1687],
[0.2168, -0.1956, 0.4050, 0.0925],
[0.2597, -0.0884, 0.1258, 0.1119],
[0.1127, -0.1203, 0.1924, 0.2859],
[0.1362, -0.1315, 0.2693, 0.1027],
[-0.3169, -0.2266, 0.4419, 0.6740],
[0.2366, -0.1452, 0.2589, 0.0579],
[0.0358, -0.2021, 0.3112, -0.1392],
],
device=torch_device,
)
self.assertTrue(torch.allclose(prediction[0, 52:64, 320:324], expected_prediction, atol=1e-4))
def test_inference_question_answering(self):
tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-base-trivia-itc")
model = BigBirdForQuestionAnswering.from_pretrained(
"google/bigbird-base-trivia-itc", attention_type="block_sparse", block_size=16, num_random_blocks=3
)
model.to(torch_device)
context = (
"The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by Zaheer, Manzil and"
" Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago"
" and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a"
" sparse-attention based transformer which extends Transformer based models, such as BERT to much longer"
" sequences. In addition to sparse attention, BigBird also applies global attention as well as random"
" attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and"
" random attention approximates full attention, while being computationally much more efficient for longer"
" sequences. As a consequence of the capability to handle longer context, BigBird has shown improved"
" performance on various long document NLP tasks, such as question answering and summarization, compared"
" to BERT or RoBERTa."
)
question = [
"Which is better for longer sequences- BigBird or BERT?",
"What is the benefit of using BigBird over BERT?",
]
inputs = tokenizer(
question,
[context, context],
padding=True,
return_tensors="pt",
add_special_tokens=True,
max_length=256,
truncation=True,
)
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
start_logits, end_logits = model(**inputs).to_tuple()
# fmt: off
target_start_logits = torch.tensor(
[[-8.5622, -9.6209, -14.3351, -8.7032, -11.8596, -7.7446, -9.6730, -13.6063, -8.9651, -11.7417, -8.2641, -8.7056, -13.4116, -5.6600, -8.8316, -10.4148, -12.2180, -7.7979, -12.5274, -6.0685, -10.3373, -11.3128, -6.6456, -14.4030, -6.8292, -14.5383, -11.5638, -6.3326, 11.5293, -1.8434, -10.0013, -7.6150], [-10.7384, -13.1179, -10.1837, -13.7700, -10.0186, -11.7335, -13.3411, -10.0188, -13.4235, -9.9381, -10.4252, -13.1281, -8.2022, -10.4326, -11.5542, -14.1549, -10.7546, -13.4691, -8.2744, -11.4324, -13.3773, -9.8284, -14.5825, -8.7471, -14.7050, -8.0364, -11.3627, -6.4638, -11.7031, -14.3446, -9.9425, -8.0088]], # noqa: E231
device=torch_device,
)
target_end_logits = torch.tensor(
[[-12.1736, -8.8487, -14.8877, -11.6713, -15.1165, -12.2396, -7.6828, -15.4153, -12.2528, -14.3671, -12.3596, -7.4272, -14.9615, -13.6356, -11.7939, -9.9767, -14.8112, -8.9567, -15.8798, -11.5291, -9.4249, -14.7544, -7.9387, -16.2789, -8.9702, -15.3111, -11.5585, -7.9992, -4.1127, 10.3209, -8.3926, -10.2005], [-11.1375, -15.4027, -12.6861, -16.9884, -13.7093, -10.3560, -15.7228, -12.9290, -15.8519, -13.7953, -10.2460, -15.7198, -14.2078, -12.8477, -11.4861, -16.1017, -11.8900, -16.4488, -13.2959, -10.3980, -15.4874, -10.3539, -16.8263, -10.9973, -17.0344, -9.2751, -10.1196, -13.8907, -12.1025, -13.0628, -12.8530, -13.8173]], # noqa: E321
device=torch_device,
)
# fmt: on
self.assertTrue(torch.allclose(start_logits[:, 64:96], target_start_logits, atol=1e-4))
self.assertTrue(torch.allclose(end_logits[:, 64:96], target_end_logits, atol=1e-4))
input_ids = inputs["input_ids"].tolist()
answer = [
input_ids[i][torch.argmax(start_logits, dim=-1)[i] : torch.argmax(end_logits, dim=-1)[i] + 1]
for i in range(len(input_ids))
]
answer = tokenizer.batch_decode(answer)
self.assertTrue(answer == ["BigBird", "global attention"])
def test_fill_mask(self):
tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
model = BigBirdForMaskedLM.from_pretrained("google/bigbird-roberta-base")
model.to(torch_device)
input_ids = tokenizer("The goal of life is [MASK] .", return_tensors="pt").input_ids.to(torch_device)
logits = model(input_ids).logits
# [MASK] is token at 6th position
pred_token = tokenizer.decode(torch.argmax(logits[0, 6:7], axis=-1))
self.assertEqual(pred_token, "happiness")
def test_auto_padding(self):
model = BigBirdModel.from_pretrained(
"google/bigbird-roberta-base", attention_type="block_sparse", num_random_blocks=3, block_size=16
)
model.to(torch_device)
model.eval()
input_ids = torch.tensor([200 * [10] + 40 * [2] + [1]], device=torch_device, dtype=torch.long)
with torch.no_grad():
output = model(input_ids).to_tuple()[0]
# fmt: off
target = torch.tensor(
[[-0.129420, -0.164740, 0.042422, -0.336030, 0.094379, 0.033794, 0.384590, 0.229660, -0.196500, 0.108020], [-0.000154, -0.168800, 0.165820, -0.313670, 0.101240, 0.035145, 0.381880, 0.213730, -0.201080, 0.077443], [0.053754, -0.166350, 0.225520, -0.272900, 0.119670, 0.019987, 0.348670, 0.199190, -0.181600, 0.084640], [0.063636, -0.187110, 0.237010, -0.297380, 0.126300, 0.020025, 0.268490, 0.191820, -0.192300, 0.035077], [0.073893, -0.184790, 0.188870, -0.297860, 0.134280, 0.028972, 0.174650, 0.186890, -0.180530, 0.006851], [0.005253, -0.169360, 0.123100, -0.302550, 0.126930, 0.024188, 0.133410, 0.200600, -0.168210, -0.001006], [-0.093336, -0.175370, -0.004768, -0.333170, 0.114330, 0.034168, 0.120960, 0.203570, -0.162810, -0.005757], [-0.160210, -0.169310, -0.049064, -0.331950, 0.115730, 0.027062, 0.143600, 0.205310, -0.144580, 0.026746], [-0.193200, -0.156820, -0.079422, -0.351600, 0.106450, 0.032174, 0.245690, 0.210250, -0.173480, 0.043914], [-0.167980, -0.153050, -0.059764, -0.357890,0.103910, 0.031481, 0.334190, 0.208960,-0.178180, 0.072165], [-0.136990, -0.156950, -0.012099, -0.353140,0.096996, 0.025864, 0.376340, 0.216050, -0.171820, 0.089963], [-0.041143, -0.167060, 0.079754, -0.353220, 0.093247, 0.019867, 0.385810, 0.214340, -0.191800, 0.065946],[0.040373, -0.158610, 0.152570, -0.312930, 0.110590, 0.012282, 0.345270, 0.204040, -0.176500, 0.064972], [0.043762, -0.166450, 0.179500, -0.317930, 0.117280, -0.004040, 0.304490, 0.201380, -0.182780, 0.044000]], # noqa: E231
device=torch_device,
)
# fmt: on
self.assertEqual(output.shape, torch.Size((1, 241, 768)))
self.assertTrue(torch.allclose(output[0, 64:78, 300:310], target, atol=0.0001))
| 43,550 | 44.650943 | 1,508 | py |
transformers | transformers-main/tests/models/big_bird/test_modeling_flax_big_bird.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class FlaxBigBirdModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=2,
seq_length=56,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=7,
hidden_act="gelu_new",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
attention_type="block_sparse",
use_bias=True,
rescale_embeddings=False,
block_size=2,
num_random_blocks=3,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
self.rescale_embeddings = rescale_embeddings
self.attention_type = attention_type
self.use_bias = use_bias
self.block_size = block_size
self.num_random_blocks = num_random_blocks
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = BigBirdConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
attention_type=self.attention_type,
block_size=self.block_size,
num_random_blocks=self.num_random_blocks,
use_bias=self.use_bias,
rescale_embeddings=self.rescale_embeddings,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_flax
class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
test_attn_probs = False
test_mismatched_shapes = False
def setUp(self):
self.model_tester = FlaxBigBirdModelTester(self)
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_from_pretrained_save_pretrained(self):
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_from_pretrained_with_no_automatic_init(self):
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_no_automatic_init(self):
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_hidden_states_output(self):
super().test_hidden_states_output()
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("google/bigbird-roberta-base")
self.assertIsNotNone(model)
def test_attention_outputs(self):
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_ids, attention_mask=None, **kwargs):
return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
# overwrite from common in order to skip the check on `attentions`
def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith("outputs.attentions"):
return
else:
super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)
| 8,604 | 37.415179 | 119 | py |
transformers | transformers-main/tests/models/big_bird/test_tokenization_big_bird.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SPIECE_UNDERLINE = "▁"
SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class BigBirdTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BigBirdTokenizer
rust_tokenizer_class = BigBirdTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
tokenizer = self.tokenizer_class(SAMPLE_VOCAB, keep_accents=True)
tokenizer.save_pretrained(self.tmpdirname)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "<s>"
token_id = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<unk>")
self.assertEqual(vocab_keys[1], "<s>")
self.assertEqual(vocab_keys[-1], "[MASK]")
self.assertEqual(len(vocab_keys), 1_004)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 1_000)
def test_rust_and_python_full_tokenizers(self):
if not self.test_rust_tokenizer:
return
tokenizer = self.get_tokenizer()
rust_tokenizer = self.get_rust_tokenizer()
sequence = "I was born in 92000, and this is falsé."
tokens = tokenizer.tokenize(sequence)
rust_tokens = rust_tokenizer.tokenize(sequence)
self.assertListEqual(tokens, rust_tokens)
ids = tokenizer.encode(sequence, add_special_tokens=False)
rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
self.assertListEqual(ids, rust_ids)
rust_tokenizer = self.get_rust_tokenizer()
ids = tokenizer.encode(sequence)
rust_ids = rust_tokenizer.encode(sequence)
self.assertListEqual(ids, rust_ids)
def test_full_tokenizer(self):
tokenizer = BigBirdTokenizer(SAMPLE_VOCAB, keep_accents=True)
tokens = tokenizer.tokenize("This is a test")
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(
tokenizer.convert_tokens_to_ids(tokens),
[285, 46, 10, 170, 382],
)
tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
self.assertListEqual(
tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
],
)
ids = tokenizer.convert_tokens_to_ids(tokens)
self.assertListEqual(
ids,
[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],
)
back_tokens = tokenizer.convert_ids_to_tokens(ids)
self.assertListEqual(
back_tokens,
[
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
],
)
@cached_property
def big_tokenizer(self):
return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
@slow
def test_tokenization_base_easy_symbols(self):
symbols = "Hello World!"
original_tokenizer_encodings = [65, 18536, 2260, 101, 66]
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@slow
def test_tokenization_base_hard_symbols(self):
symbols = (
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
# fmt: off
original_tokenizer_encodings = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66] # noqa: E231
# fmt: on
self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols))
@require_torch
@slow
def test_torch_encode_plus_sent_to_model(self):
import torch
from transformers import BigBirdConfig, BigBirdModel
# Build sequence
first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10]
sequence = " ".join(first_ten_tokens)
encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False)
batch_encoded_sequence = self.big_tokenizer.batch_encode_plus(
[sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False
)
config = BigBirdConfig(attention_type="original_full")
model = BigBirdModel(config)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**encoded_sequence)
model(**batch_encoded_sequence)
@slow
def test_special_tokens(self):
"""
To reproduce:
$ wget https://github.com/google-research/bigbird/blob/master/bigbird/vocab/gpt2.model?raw=true
$ mv gpt2.model?raw=true gpt2.model
```
import tensorflow_text as tft
import tensorflow as tf
vocab_model_file = "./gpt2.model"
tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(vocab_model_file, "rb").read()))
ids = tokenizer.tokenize("Paris is the [MASK].")
ids = tf.concat([tf.constant([65]), ids, tf.constant([66])], axis=0)
detokenized = tokenizer.detokenize(ids) # should give [CLS] Paris is the [MASK].[SEP]
"""
tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
decoded_text = tokenizer.decode(tokenizer("Paris is the [MASK].").input_ids)
self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]")
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="google/bigbird-roberta-base",
revision="215c99f1600e06f83acce68422f2035b2b5c3510",
)
| 10,691 | 44.113924 | 2,209 | py |
transformers | transformers-main/tests/models/xlm/test_modeling_xlm.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class XLMModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_lengths=True,
use_token_type_ids=True,
use_labels=True,
gelu_activation=True,
sinusoidal_embeddings=False,
causal=False,
asm=False,
n_langs=2,
vocab_size=99,
n_special=0,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=2,
num_choices=4,
summary_type="last",
use_proj=True,
scope=None,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_lengths = use_input_lengths
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.gelu_activation = gelu_activation
self.sinusoidal_embeddings = sinusoidal_embeddings
self.causal = causal
self.asm = asm
self.n_langs = n_langs
self.vocab_size = vocab_size
self.n_special = n_special
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.summary_type = summary_type
self.use_proj = use_proj
self.scope = scope
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = random_attention_mask([self.batch_size, self.seq_length])
input_lengths = None
if self.use_input_lengths:
input_lengths = (
ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2
) # small variation of seq_length
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs)
sequence_labels = None
token_labels = None
is_impossible_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
is_impossible_labels = ids_tensor([self.batch_size], 2).float()
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def get_config(self):
return XLMConfig(
vocab_size=self.vocab_size,
n_special=self.n_special,
emb_dim=self.hidden_size,
n_layers=self.num_hidden_layers,
n_heads=self.num_attention_heads,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
gelu_activation=self.gelu_activation,
sinusoidal_embeddings=self.sinusoidal_embeddings,
asm=self.asm,
causal=self.causal,
n_langs=self.n_langs,
max_position_embeddings=self.max_position_embeddings,
initializer_range=self.initializer_range,
summary_type=self.summary_type,
use_proj=self.use_proj,
num_labels=self.num_labels,
bos_token_id=self.bos_token_id,
)
def create_and_check_xlm_model(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, lengths=input_lengths, langs=token_type_ids)
result = model(input_ids, langs=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_xlm_lm_head(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMWithLMHeadModel(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_xlm_simple_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnsweringSimple(config)
model.to(torch_device)
model.eval()
outputs = model(input_ids)
outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
result = outputs
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_xlm_qa(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForQuestionAnswering(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
p_mask=input_mask,
)
result_with_labels = model(
input_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
cls_index=sequence_labels,
is_impossible=is_impossible_labels,
)
(total_loss,) = result_with_labels.to_tuple()
result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels)
(total_loss,) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape, ())
self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top))
self.parent.assertEqual(
result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(
result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top)
)
self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,))
def create_and_check_xlm_sequence_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
model = XLMForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids)
result = model(input_ids, labels=sequence_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def create_and_check_xlm_token_classif(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_labels = self.num_labels
model = XLMForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_xlm_for_multiple_choice(
self,
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
):
config.num_choices = self.num_choices
model = XLMForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class XLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
pipeline_model_mapping = (
{
"feature-extraction": XLMModel,
"fill-mask": XLMWithLMHeadModel,
"question-answering": XLMForQuestionAnsweringSimple,
"text-classification": XLMForSequenceClassification,
"text-generation": XLMWithLMHeadModel,
"token-classification": XLMForTokenClassification,
"zero-shot": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
# XLM has 2 QA models -> need to manually set the correct labels for one of them here
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
inputs_dict["start_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
inputs_dict["end_positions"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = XLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_lm_head(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs)
def test_xlm_simple_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*config_and_inputs)
def test_xlm_qa(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*config_and_inputs)
def test_xlm_sequence_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*config_and_inputs)
def test_xlm_token_classif(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*config_and_inputs)
def test_xlm_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*config_and_inputs)
def _check_attentions_for_generate(
self, batch_size, attentions, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(attentions, tuple)
self.assertListEqual(
[isinstance(iter_attentions, tuple) for iter_attentions in attentions], [True] * len(attentions)
)
self.assertEqual(len(attentions), (max_length - min_length) * num_beam_groups)
for idx, iter_attentions in enumerate(attentions):
# adds PAD dummy token
tgt_len = min_length + idx + 1
src_len = min_length + idx + 1
expected_shape = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions], [expected_shape] * len(iter_attentions)
)
def _check_hidden_states_for_generate(
self, batch_size, hidden_states, min_length, max_length, config, use_cache=False, num_beam_groups=1
):
self.assertIsInstance(hidden_states, tuple)
self.assertListEqual(
[isinstance(iter_hidden_states, tuple) for iter_hidden_states in hidden_states],
[True] * len(hidden_states),
)
self.assertEqual(len(hidden_states), (max_length - min_length) * num_beam_groups)
for idx, iter_hidden_states in enumerate(hidden_states):
# adds PAD dummy token
seq_len = min_length + idx + 1
expected_shape = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],
[expected_shape] * len(iter_hidden_states),
)
pass
@slow
def test_model_from_pretrained(self):
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = XLMModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class XLMModelLanguageGenerationTest(unittest.TestCase):
@slow
def test_lm_generate_xlm_mlm_en_2048(self):
model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
model.to(torch_device)
input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device) # the president
expected_output_ids = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].cpu().numpy().tolist(), expected_output_ids)
| 19,709 | 35.165138 | 166 | py |
transformers | transformers-main/tests/models/speech_encoder_decoder/test_modeling_speech_encoder_decoder.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_bert import BertModelTester
from ..speech_to_text.test_modeling_speech_to_text import Speech2TextModelTester
from ..speech_to_text_2.test_modeling_speech_to_text_2 import Speech2Text2StandaloneDecoderModelTester
from ..wav2vec2.test_modeling_wav2vec2 import Wav2Vec2ModelTester
if is_torch_available():
import numpy as np
import torch
from transformers import (
BertLMHeadModel,
Speech2Text2ForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Model,
)
from transformers.modeling_outputs import BaseModelOutput
from transformers.models.speech_to_text.modeling_speech_to_text import Speech2TextEncoder
@require_torch
class EncoderDecoderMixin:
def get_encoder_decoder_model(self, config, decoder_config):
pass
def prepare_config_and_inputs(self):
pass
def get_pretrained_model_and_inputs(self):
pass
def check_encoder_decoder_model_from_pretrained_configs(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
enc_dec_model = SpeechEncoderDecoderModel(encoder_decoder_config)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
self.assertFalse(enc_dec_model.config.tie_word_embeddings)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
encoder_outputs = BaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1])
outputs_encoder_decoder = enc_dec_model(
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_with_inputs(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
inputs = input_values if input_features is None else input_features
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
outputs_encoder_decoder_kwarg = enc_dec_model(
inputs=inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
)
self.assertEqual(
outputs_encoder_decoder_kwarg["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_from_pretrained(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
return_dict,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
enc_dec_model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
return_dict=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_save_and_load(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
enc_dec_model.save_pretrained(tmpdirname)
enc_dec_model = SpeechEncoderDecoderModel.from_pretrained(tmpdirname)
enc_dec_model.to(torch_device)
after_outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_save_and_load_encoder_decoder_model(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
input_values=None,
input_features=None,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
)
after_outputs = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_encoder_decoder_model_output_attentions(
self,
config,
attention_mask,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
labels=None,
input_values=None,
input_features=None,
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
decoder_attention_mask = decoder_attention_mask[:, :-1]
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_values=input_values,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
output_attentions=True,
)
inputs = input_values if input_features is None else input_features
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
seq_len = enc_dec_model.encoder._get_feat_extract_output_lengths(inputs.shape[1])
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
num_decoder_layers = (
decoder_config.num_decoder_layers
if hasattr(decoder_config, "num_decoder_layers")
else decoder_config.num_hidden_layers
)
self.assertEqual(len(decoder_attentions), num_decoder_layers)
self.assertEqual(
decoder_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
)
cross_attentions = outputs_encoder_decoder["cross_attentions"]
self.assertEqual(len(cross_attentions), num_decoder_layers)
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
self.assertEqual(
cross_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
)
def check_encoder_decoder_model_generate(
self, config, decoder_config, input_values=None, input_features=None, **kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
# make sure EOS token is set to None to prevent early stopping of generation
if hasattr(enc_dec_model.config, "eos_token_id"):
enc_dec_model.config.eos_token_id = None
if hasattr(enc_dec_model.config, "decoder") and hasattr(enc_dec_model.config.decoder, "eos_token_id"):
enc_dec_model.config.decoder.eos_token_id = None
inputs = input_values if input_features is None else input_features
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
inputs, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
)
self.assertEqual(generated_output.shape, (inputs.shape[0],) + (decoder_config.max_length,))
def test_encoder_decoder_model(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_with_inputs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_with_inputs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained_configs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)
def test_encoder_decoder_model_from_pretrained_return_dict(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)
def test_save_and_load_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load(**input_ids_dict)
def test_save_and_load_from_encoder_decoder_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_output_attentions(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_output_attentions(**input_ids_dict)
def test_encoder_decoder_model_generate(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_generate(**input_ids_dict)
def test_training_gradient_checkpointing(self):
inputs_dict = self.prepare_config_and_inputs()
encoder_model, decoder_model = self.get_encoder_decoder_model(
inputs_dict["config"], inputs_dict["decoder_config"]
)
model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
model.to(torch_device)
model.train()
model.gradient_checkpointing_enable()
model.config.decoder_start_token_id = 0
model.config.pad_token_id = 0
model_inputs = {
"attention_mask": inputs_dict["attention_mask"],
"labels": inputs_dict["labels"],
"decoder_input_ids": inputs_dict["decoder_input_ids"],
}
inputs = inputs_dict["input_features"] if "input_features" in inputs_dict else inputs_dict["input_values"]
loss = model(inputs, **model_inputs).loss
loss.backward()
@slow
def test_real_model_save_load_from_pretrained(self):
model_2, inputs = self.get_pretrained_model_and_inputs()
model_2.to(torch_device)
with torch.no_grad():
outputs = model_2(**inputs)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = SpeechEncoderDecoderModel.from_pretrained(tmp_dirname)
model_1.to(torch_device)
after_outputs = model_1(**inputs)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
@require_torch
class Wav2Vec2BertModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-base-960h", "bert-base-cased"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"input_values": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = Wav2Vec2Model(config).eval()
decoder_model = BertLMHeadModel(decoder_config).eval()
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
bert_model_tester = BertModelTester(self)
wav2vec2_model_tester = Wav2Vec2ModelTester(self)
encoder_config_and_inputs = wav2vec2_model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
(
config,
input_values,
input_mask,
) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_token_type_ids,
decoder_input_mask,
decoder_sequence_labels,
decoder_token_labels,
decoder_choice_labels,
encoder_attention_mask,
_,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"input_values": input_values,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_token_type_ids": decoder_token_type_ids,
"decoder_attention_mask": decoder_input_mask,
"decoder_sequence_labels": decoder_sequence_labels,
"decoder_token_labels": decoder_token_labels,
"decoder_choice_labels": decoder_choice_labels,
"labels": decoder_token_labels,
}
@require_torch
class Speech2TextBertModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/s2t-small-librispeech-asr", "bert-base-cased"
)
batch_size = 13
input_features = floats_tensor([batch_size, 7, 80], scale=1.0)
attention_mask = random_attention_mask([batch_size, 7])
decoder_input_ids = ids_tensor([batch_size, 4], model.decoder.config.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"input_features": input_features,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = Speech2TextEncoder(config).eval()
decoder_model = BertLMHeadModel(decoder_config).eval()
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
bert_model_tester = BertModelTester(self)
speech2text_model_tester = Speech2TextModelTester(self)
encoder_config_and_inputs = speech2text_model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
config, inputs = encoder_config_and_inputs
input_features = inputs["input_features"]
input_mask = inputs["attention_mask"]
(
decoder_config,
decoder_input_ids,
decoder_token_type_ids,
decoder_input_mask,
decoder_sequence_labels,
decoder_token_labels,
decoder_choice_labels,
encoder_attention_mask,
_,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"input_features": input_features,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_token_type_ids": decoder_token_type_ids,
"decoder_attention_mask": decoder_input_mask,
"decoder_sequence_labels": decoder_sequence_labels,
"decoder_token_labels": decoder_token_labels,
"decoder_choice_labels": decoder_choice_labels,
"labels": decoder_token_labels,
}
# can't save full model for now because Speech2TextModel != Speech2TextEncoder
def test_encoder_decoder_model_from_pretrained_configs(self):
pass
# can't save full model for now because Speech2TextModel != Speech2TextEncoder
def test_save_and_load_from_pretrained(self):
pass
# all published pretrained models are Speech2TextModel != Speech2TextEncoder
def test_real_model_save_load_from_pretrained(self):
pass
@require_torch
class Wav2Vec2Speech2Text2(EncoderDecoderMixin, unittest.TestCase):
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = Wav2Vec2Model(config).eval()
decoder_model = Speech2Text2ForCausalLM(decoder_config).eval()
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester_encoder = Wav2Vec2ModelTester(self, batch_size=13)
model_tester_decoder = Speech2Text2StandaloneDecoderModelTester(
self, batch_size=13, d_model=32, max_position_embeddings=512
)
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs()
(
config,
input_values,
input_mask,
) = encoder_config_and_inputs
(decoder_config, decoder_input_ids, decoder_attention_mask, _) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
# disable cache for now
decoder_config.use_cache = False
return {
"config": config,
"input_values": input_values,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"labels": decoder_input_ids,
}
# there are no published pretrained Speech2Text2ForCausalLM for now
def test_real_model_save_load_from_pretrained(self):
pass
| 25,247 | 39.59164 | 124 | py |
transformers | transformers-main/tests/models/speech_encoder_decoder/test_modeling_flax_speech_encoder_decoder.py | # coding=utf-8
# Copyright 2022 HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow, torch_device
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bart.test_modeling_flax_bart import FlaxBartStandaloneDecoderModelTester
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..gpt2.test_modeling_flax_gpt2 import FlaxGPT2ModelTester
from ..wav2vec2.test_modeling_flax_wav2vec2 import FlaxWav2Vec2ModelTester
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.training.common_utils import onehot
from flax.traverse_util import flatten_dict
from transformers import (
FlaxBartForCausalLM,
FlaxBertForCausalLM,
FlaxGPT2LMHeadModel,
FlaxSpeechEncoderDecoderModel,
FlaxWav2Vec2Model,
SpeechEncoderDecoderConfig,
)
from transformers.modeling_flax_outputs import FlaxBaseModelOutput
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import SpeechEncoderDecoderModel
@require_flax
class FlaxEncoderDecoderMixin:
def get_encoder_decoder_model(self, config, decoder_config):
raise NotImplementedError
def prepare_config_and_inputs(self):
raise NotImplementedError
def get_pretrained_model(self):
raise NotImplementedError
def check_encoder_decoder_model_from_pretrained_configs(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
enc_dec_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
self.assertFalse(enc_dec_model.config.tie_word_embeddings)
outputs_encoder_decoder = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = SpeechEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
outputs_encoder_decoder = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
encoder_outputs = FlaxBaseModelOutput(last_hidden_state=outputs_encoder_decoder.encoder_hidden_states[-1])
outputs_encoder_decoder = enc_dec_model(
attention_mask, decoder_input_ids, decoder_attention_mask, encoder_outputs=encoder_outputs
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_from_pretrained(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
return_dict,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model, "return_dict": return_dict}
enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
outputs_encoder_decoder = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
return_dict=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_save_and_load(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
outputs = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = np.array(outputs[0])
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
enc_dec_model.save_pretrained(tmpdirname)
FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname)
after_outputs = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = np.array(after_outputs[0])
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 4e-2)
def check_encoder_decoder_model_from_encoder_decoder_pretrained(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
# assert that loading encoder and decoder models from configs has been correctly executed
self.assertEqual(config.add_adapter, encoder_model.config.add_adapter)
self.assertEqual(decoder_config.use_cache, decoder_model.config.use_cache)
with tempfile.TemporaryDirectory() as enc_tmpdir:
with tempfile.TemporaryDirectory() as dec_tmpdir:
encoder_model.save_pretrained(enc_tmpdir)
decoder_model.save_pretrained(dec_tmpdir)
# load a model from pretrained encoder and decoder checkpoints, setting one encoder and one decoder kwarg opposite to that specified in their respective configs
enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=enc_tmpdir,
decoder_pretrained_model_name_or_path=dec_tmpdir,
encoder_add_adapter=not config.add_adapter,
decoder_use_cache=not decoder_config.use_cache,
)
# assert that setting encoder and decoder kwargs opposite to those in the configs has correctly been applied
self.assertNotEqual(config.add_adapter, enc_dec_model.config.encoder.add_adapter)
self.assertNotEqual(decoder_config.use_cache, enc_dec_model.config.decoder.use_cache)
outputs_encoder_decoder = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_hidden_states=True,
return_dict=True,
)
self.assertEqual(
outputs_encoder_decoder["logits"].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,))
)
def check_encoder_decoder_model_output_attentions(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
# make the decoder inputs a different shape from the encoder inputs to harden the test
decoder_input_ids = decoder_input_ids[:, :-1]
decoder_attention_mask = decoder_attention_mask[:, :-1]
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
outputs_encoder_decoder = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
output_attentions=True,
)
encoder_attentions = outputs_encoder_decoder["encoder_attentions"]
self.assertEqual(len(encoder_attentions), config.num_hidden_layers)
seq_len = enc_dec_model._get_feat_extract_output_lengths(inputs.shape[1])
self.assertEqual(encoder_attentions[0].shape[-3:], (config.num_attention_heads, seq_len, seq_len))
decoder_attentions = outputs_encoder_decoder["decoder_attentions"]
num_decoder_layers = (
decoder_config.num_decoder_layers
if hasattr(decoder_config, "num_decoder_layers")
else decoder_config.num_hidden_layers
)
self.assertEqual(len(decoder_attentions), num_decoder_layers)
self.assertEqual(
decoder_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, decoder_input_ids.shape[-1], decoder_input_ids.shape[-1]),
)
cross_attentions = outputs_encoder_decoder["cross_attentions"]
self.assertEqual(len(cross_attentions), num_decoder_layers)
cross_attention_input_seq_len = decoder_input_ids.shape[-1]
self.assertEqual(
cross_attentions[0].shape[-3:],
(decoder_config.num_attention_heads, cross_attention_input_seq_len, seq_len),
)
def check_encoder_decoder_model_generate(self, inputs, config, decoder_config, **kwargs):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
enc_dec_model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
pad_token_id = enc_dec_model.config.decoder.pad_token_id
eos_token_id = enc_dec_model.config.decoder.eos_token_id
decoder_start_token_id = enc_dec_model.config.decoder.decoder_start_token_id
# Copied from generation.utils (GPT2 doesn't have `pad_token_id`)
if pad_token_id is None and eos_token_id is not None:
pad_token_id = eos_token_id
if decoder_start_token_id is None:
decoder_start_token_id = enc_dec_model.config.decoder.bos_token_id
# Bert does not have a bos token id, so use pad_token_id instead
# Copied from `test_modeling_encoder_decoder.py`
if decoder_start_token_id is None:
decoder_start_token_id = pad_token_id
generated_output = enc_dec_model.generate(
inputs,
pad_token_id=pad_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
)
generated_sequences = generated_output.sequences
self.assertEqual(generated_sequences.shape, (inputs.shape[0],) + (decoder_config.max_length,))
def check_freeze_feature_encoder(
self,
config,
inputs,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs,
):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
enc_dec_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config)
params = enc_dec_model.params
def cross_entropy(logits, labels):
return -jnp.sum(labels * jax.nn.log_softmax(logits, axis=-1), axis=-1)
# define a dummy loss function for computing the loss over a forward pass
def compute_loss(
params,
inputs,
attention_mask,
decoder_input_ids,
freeze_feature_encoder: bool = False,
):
outputs_enc_dec = enc_dec_model(
inputs=inputs,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
freeze_feature_encoder=freeze_feature_encoder,
params=params,
)
logits = outputs_enc_dec.logits
vocab_size = logits.shape[-1]
loss = cross_entropy(logits, onehot(labels=decoder_input_ids, num_classes=vocab_size)).sum()
return (loss, logits)
# transform the loss function to get the gradients
grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
# compute the loss, logits, and gradients for the unfrozen model
(loss, logits), grads = grad_fn(
params, inputs, attention_mask, decoder_input_ids, freeze_feature_encoder=False
)
# compare to the loss, logits and gradients for the frozen model
(loss_frozen, logits_frozen), grads_frozen = grad_fn(
params, inputs, attention_mask, decoder_input_ids, freeze_feature_encoder=True
)
# ensure that the logits and losses remain precisely equal
self.assertTrue((logits == logits_frozen).all())
self.assertEqual(loss, loss_frozen)
grads = flatten_dict(grads)
grads_frozen = flatten_dict(grads_frozen)
# ensure that the dicts of gradients contain the same keys
self.assertEqual(grads.keys(), grads_frozen.keys())
# ensure that the gradients of the feature extractor layers are precisely zero when frozen and contain non-zero entries when unfrozen
feature_extractor_grads = tuple(grads[k] for k in grads if "feature_extractor" in k)
feature_extractor_grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" in k)
for feature_extractor_grad, feature_extractor_grad_frozen in zip(
feature_extractor_grads, feature_extractor_grads_frozen
):
self.assertTrue((feature_extractor_grad_frozen == 0.0).all())
self.assertTrue((feature_extractor_grad > 0.0).any())
# ensure that the gradients of all unfrozen layers remain precisely equal, i.e. all layers excluding the frozen 'feature_extractor'
grads = tuple(grads[k] for k in grads if "feature_extractor" not in k)
grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" not in k)
for grad, grad_frozen in zip(grads, grads_frozen):
self.assertTrue((grad == grad_frozen).all())
def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict):
pt_model.to(torch_device)
pt_model.eval()
# prepare inputs
flax_inputs = inputs_dict
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**inputs_dict).to_tuple()
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(), 1e-5)
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 1e-5)
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
for fx_output, pt_output_loaded in zip(fx_outputs, pt_outputs_loaded):
self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 1e-5)
def check_equivalence_pt_to_flax(self, config, decoder_config, inputs_dict):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
pt_model = SpeechEncoderDecoderModel(encoder_decoder_config)
fx_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config)
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
def check_equivalence_flax_to_pt(self, config, decoder_config, inputs_dict):
encoder_decoder_config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
pt_model = SpeechEncoderDecoderModel(encoder_decoder_config)
fx_model = FlaxSpeechEncoderDecoderModel(encoder_decoder_config)
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict)
def test_encoder_decoder_model_from_pretrained_configs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=False)
def test_encoder_decoder_model_from_pretrained_return_dict(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict, return_dict=True)
def test_save_and_load_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load(**input_ids_dict)
def test_encoder_decoder_model_from_encoder_decoder_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_encoder_decoder_pretrained(**input_ids_dict)
def test_encoder_decoder_model_output_attentions(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_output_attentions(**input_ids_dict)
def test_freeze_feature_encoder(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_freeze_feature_encoder(**input_ids_dict)
def test_encoder_decoder_model_generate(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_generate(**input_ids_dict)
def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
diff = np.abs((a - b)).max()
self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
@is_pt_flax_cross_test
def test_pt_flax_equivalence(self):
config_inputs_dict = self.prepare_config_and_inputs()
config = config_inputs_dict.pop("config")
decoder_config = config_inputs_dict.pop("decoder_config")
inputs_dict = config_inputs_dict
# `encoder_hidden_states` is not used in model call/forward
del inputs_dict["encoder_hidden_states"]
# Avoid the case where a sequence has no place to attend (after combined with the causal attention mask)
batch_size = inputs_dict["decoder_attention_mask"].shape[0]
inputs_dict["decoder_attention_mask"] = np.concatenate(
[np.ones(shape=(batch_size, 1)), inputs_dict["decoder_attention_mask"][:, 1:]], axis=1
)
# Flax models don't use the `use_cache` option and cache is not returned as a default.
# So we disable `use_cache` here for PyTorch model.
decoder_config.use_cache = False
self.assertTrue(decoder_config.cross_attention_hidden_size is None)
# check without `enc_to_dec_proj` projection
decoder_config.hidden_size = config.hidden_size
self.assertTrue(config.hidden_size == decoder_config.hidden_size)
self.check_equivalence_pt_to_flax(config, decoder_config, inputs_dict)
self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict)
# check `enc_to_dec_proj` work as expected
decoder_config.hidden_size = decoder_config.hidden_size * 2
self.assertTrue(config.hidden_size != decoder_config.hidden_size)
self.check_equivalence_pt_to_flax(config, decoder_config, inputs_dict)
self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict)
# check `add_adapter` works as expected
config.add_adapter = True
self.assertTrue(config.add_adapter)
self.check_equivalence_pt_to_flax(config, decoder_config, inputs_dict)
self.check_equivalence_flax_to_pt(config, decoder_config, inputs_dict)
@slow
def test_real_model_save_load_from_pretrained(self):
model_2 = self.get_pretrained_model()
inputs = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
decoder_input_ids = ids_tensor([13, 1], model_2.config.decoder.vocab_size)
attention_mask = ids_tensor([13, 5], vocab_size=2)
outputs = model_2(
inputs=inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
out_2 = np.array(outputs[0])
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = FlaxSpeechEncoderDecoderModel.from_pretrained(tmp_dirname)
after_outputs = model_1(
inputs=inputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
out_1 = np.array(after_outputs[0])
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 4e-2)
@require_flax
class FlaxWav2Vec2GPT2ModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-large-lv60", "gpt2-medium"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = FlaxWav2Vec2Model(config)
decoder_model = FlaxGPT2LMHeadModel(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester_encoder = FlaxWav2Vec2ModelTester(self, batch_size=13)
model_tester_decoder = FlaxGPT2ModelTester(self, batch_size=13)
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
(config, inputs, attention_mask) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"inputs": inputs,
"attention_mask": attention_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"encoder_hidden_states": encoder_hidden_states,
}
@slow
def test_flaxwav2vec2gpt2_pt_flax_equivalence(self):
pt_model = SpeechEncoderDecoderModel.from_pretrained("jsnfly/wav2vec2-large-xlsr-53-german-gpt2")
fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained(
"jsnfly/wav2vec2-large-xlsr-53-german-gpt2", from_pt=True
)
pt_model.to(torch_device)
pt_model.eval()
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs_dict = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
flax_inputs = inputs_dict
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_outputs = pt_outputs.to_tuple()
fx_outputs = fx_model(**inputs_dict)
fx_logits = fx_outputs.logits
fx_outputs = fx_outputs.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**inputs_dict)
fx_logits_loaded = fx_outputs_loaded.logits
fx_outputs_loaded = fx_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2)
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
pt_logits_loaded = pt_outputs_loaded.logits
pt_outputs_loaded = pt_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
@require_flax
class FlaxWav2Vec2BartModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-large-lv60", "bart-large"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = FlaxWav2Vec2Model(config)
decoder_model = FlaxBartForCausalLM(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester_encoder = FlaxWav2Vec2ModelTester(self, batch_size=13)
model_tester_decoder = FlaxBartStandaloneDecoderModelTester(self, batch_size=13)
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
(config, inputs, attention_mask) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"inputs": inputs,
"attention_mask": attention_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"encoder_hidden_states": encoder_hidden_states,
}
@slow
def test_flaxwav2vec2bart_pt_flax_equivalence(self):
pt_model = SpeechEncoderDecoderModel.from_pretrained("patrickvonplaten/wav2vec2-2-bart-large")
fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained(
"patrickvonplaten/wav2vec2-2-bart-large", from_pt=True
)
pt_model.to(torch_device)
pt_model.eval()
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], scale=1.0)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs_dict = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
flax_inputs = inputs_dict
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_outputs = pt_outputs.to_tuple()
fx_outputs = fx_model(**inputs_dict)
fx_logits = fx_outputs.logits
fx_outputs = fx_outputs.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**inputs_dict)
fx_logits_loaded = fx_outputs_loaded.logits
fx_outputs_loaded = fx_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2)
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
pt_logits_loaded = pt_outputs_loaded.logits
pt_outputs_loaded = pt_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
@require_flax
class FlaxWav2Vec2BertModelTest(FlaxEncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model_and_inputs(self):
model = FlaxSpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
"facebook/wav2vec2-large-lv60", "bert-large-uncased"
)
batch_size = 13
input_values = floats_tensor([batch_size, 512], model.config.encoder.vocab_size)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
return model, inputs
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = FlaxWav2Vec2Model(config)
decoder_model = FlaxBertForCausalLM(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester_encoder = FlaxWav2Vec2ModelTester(self, batch_size=13)
model_tester_decoder = FlaxBertModelTester(self, batch_size=13)
encoder_config_and_inputs = model_tester_encoder.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester_decoder.prepare_config_and_inputs_for_decoder()
(config, inputs, attention_mask) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_attention_mask,
encoder_hidden_states,
encoder_attention_mask,
) = decoder_config_and_inputs
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"inputs": inputs,
"attention_mask": attention_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"encoder_hidden_states": encoder_hidden_states,
}
@slow
def test_flaxwav2vec2bert_pt_flax_equivalence(self):
pt_model = SpeechEncoderDecoderModel.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large")
fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained("speech-seq2seq/wav2vec2-2-bert-large", from_pt=True)
pt_model.to(torch_device)
pt_model.eval()
# prepare inputs
batch_size = 13
input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size)
attention_mask = random_attention_mask([batch_size, 512])
decoder_input_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_attention_mask = random_attention_mask([batch_size, 4])
inputs_dict = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
}
flax_inputs = inputs_dict
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_outputs = pt_outputs.to_tuple()
fx_outputs = fx_model(**inputs_dict)
fx_logits = fx_outputs.logits
fx_outputs = fx_outputs.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits.numpy(), 4e-2)
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**inputs_dict)
fx_logits_loaded = fx_outputs_loaded.logits
fx_outputs_loaded = fx_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits_loaded, pt_logits.numpy(), 4e-2)
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = SpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_flax=True)
pt_model_loaded.to(torch_device)
pt_model_loaded.eval()
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs)
pt_logits_loaded = pt_outputs_loaded.logits
pt_outputs_loaded = pt_outputs_loaded.to_tuple()
self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch")
self.assert_almost_equals(fx_logits, pt_logits_loaded.numpy(), 4e-2)
| 39,515 | 41.766234 | 176 | py |
transformers | transformers-main/tests/models/speech_to_text_2/test_modeling_speech_to_text_2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Speech2Text model. """
import unittest
from transformers import Speech2Text2Config
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.speech_to_text_2.modeling_speech_to_text_2 import (
Speech2Text2Decoder,
Speech2Text2ForCausalLM,
)
@require_torch
class Speech2Text2StandaloneDecoderModelTester:
def __init__(
self,
parent,
vocab_size=99,
batch_size=13,
d_model=16,
decoder_seq_length=7,
is_training=True,
is_decoder=True,
use_attention_mask=True,
use_cache=False,
use_labels=True,
decoder_start_token_id=2,
decoder_ffn_dim=32,
decoder_layers=4,
decoder_attention_heads=4,
max_position_embeddings=30,
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.decoder_seq_length = decoder_seq_length
# For common tests
self.seq_length = self.decoder_seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.d_model = d_model
self.hidden_size = d_model
self.num_hidden_layers = decoder_layers
self.decoder_layers = decoder_layers
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_attention_heads = decoder_attention_heads
self.num_attention_heads = decoder_attention_heads
self.eos_token_id = eos_token_id
self.bos_token_id = bos_token_id
self.pad_token_id = pad_token_id
self.decoder_start_token_id = decoder_start_token_id
self.use_cache = use_cache
self.max_position_embeddings = max_position_embeddings
self.scope = None
self.decoder_key_length = decoder_seq_length
self.base_model_out_len = 2
self.decoder_attention_idx = 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
lm_labels = None
if self.use_labels:
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
config = Speech2Text2Config(
vocab_size=self.vocab_size,
d_model=self.d_model,
decoder_layers=self.decoder_layers,
decoder_ffn_dim=self.decoder_ffn_dim,
decoder_attention_heads=self.decoder_attention_heads,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
use_cache=self.use_cache,
pad_token_id=self.pad_token_id,
decoder_start_token_id=self.decoder_start_token_id,
max_position_embeddings=self.max_position_embeddings,
)
return (
config,
input_ids,
attention_mask,
lm_labels,
)
def create_and_check_decoder_model_past(
self,
config,
input_ids,
attention_mask,
lm_labels,
):
config.use_cache = True
model = Speech2Text2Decoder(config=config).to(torch_device).eval()
input_ids = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
outputs = model(input_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids)
outputs_no_past = model(input_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
past_key_values = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((2, 1), config.vocab_size - 1) + 1
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
print(next_input_ids)
output_from_no_past = model(next_input_ids)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
attention_mask,
lm_labels,
) = config_and_inputs
inputs_dict = {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
return config, inputs_dict
@require_torch
class Speech2Text2StandaloneDecoderModelTest(
ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
):
all_model_classes = (Speech2Text2Decoder, Speech2Text2ForCausalLM) if is_torch_available() else ()
all_generative_model_classes = (Speech2Text2ForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = {"text-generation": Speech2Text2ForCausalLM} if is_torch_available() else {}
fx_compatible = True
test_pruning = False
def setUp(
self,
):
self.model_tester = Speech2Text2StandaloneDecoderModelTester(self, is_training=False)
self.config_tester = ConfigTester(self, config_class=Speech2Text2Config)
# not implemented currently
def test_inputs_embeds(self):
pass
# speech2text2 has no base model
def test_save_load_fast_init_from_base(self):
pass
# speech2text2 has no base model
def test_save_load_fast_init_to_base(self):
pass
def test_config(self):
self.config_tester.run_common_tests()
def test_decoder_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)
# decoder cannot keep gradients
def test_retain_grad_hidden_states_attentions(self):
return
| 7,543 | 33.764977 | 115 | py |
transformers | transformers-main/tests/models/bark/test_modeling_bark.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Bark model. """
import copy
import inspect
import tempfile
import unittest
from transformers import (
BarkCoarseConfig,
BarkFineConfig,
BarkSemanticConfig,
is_torch_available,
)
from transformers.models.bark.generation_configuration_bark import (
BarkCoarseGenerationConfig,
BarkFineGenerationConfig,
BarkSemanticGenerationConfig,
)
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
BarkCausalModel,
BarkCoarseModel,
BarkFineModel,
BarkModel,
BarkProcessor,
BarkSemanticModel,
)
class BarkSemanticModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=4,
is_training=False, # for now training is not supported
use_input_mask=True,
use_labels=True,
vocab_size=33,
output_vocab_size=33,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=15,
dropout=0.1,
window_size=256,
initializer_range=0.02,
n_codes_total=8, # for BarkFineModel
n_codes_given=1, # for BarkFineModel
config_class=None,
model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.output_vocab_size = output_vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.window_size = window_size
self.initializer_range = initializer_range
self.bos_token_id = output_vocab_size - 1
self.eos_token_id = output_vocab_size - 1
self.pad_token_id = output_vocab_size - 1
self.n_codes_total = n_codes_total
self.n_codes_given = n_codes_given
self.is_encoder_decoder = False
self.config_class = config_class
self.model_class = model_class
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
inputs_dict = {
"input_ids": input_ids,
"head_mask": head_mask,
"attention_mask": input_mask,
}
return config, inputs_dict
def get_config(self):
return self.config_class(
vocab_size=self.vocab_size,
output_vocab_size=self.output_vocab_size,
hidden_size=self.hidden_size,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
window_size=self.window_size,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = self.model_class(config=config).to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"logits"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
# test no attention_mask works
outputs = model(input_ids, use_cache=True)
_, past_key_values = outputs.to_tuple()
output_from_no_past = model(next_input_ids)["logits"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"]
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
class BarkCoarseModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=4,
is_training=False, # for now training is not supported
use_input_mask=True,
use_labels=True,
vocab_size=33,
output_vocab_size=33,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=15,
dropout=0.1,
window_size=256,
initializer_range=0.02,
n_codes_total=8, # for BarkFineModel
n_codes_given=1, # for BarkFineModel
config_class=None,
model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.output_vocab_size = output_vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.window_size = window_size
self.initializer_range = initializer_range
self.bos_token_id = output_vocab_size - 1
self.eos_token_id = output_vocab_size - 1
self.pad_token_id = output_vocab_size - 1
self.n_codes_total = n_codes_total
self.n_codes_given = n_codes_given
self.is_encoder_decoder = False
self.config_class = config_class
self.model_class = model_class
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
inputs_dict = {
"input_ids": input_ids,
"head_mask": head_mask,
"attention_mask": input_mask,
}
return config, inputs_dict
def get_config(self):
return self.config_class(
vocab_size=self.vocab_size,
output_vocab_size=self.output_vocab_size,
hidden_size=self.hidden_size,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
window_size=self.window_size,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = self.model_class(config=config).to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"logits"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
# test no attention_mask works
outputs = model(input_ids, use_cache=True)
_, past_key_values = outputs.to_tuple()
output_from_no_past = model(next_input_ids)["logits"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"]
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
class BarkFineModelTester:
def __init__(
self,
parent,
batch_size=2,
seq_length=4,
is_training=False, # for now training is not supported
use_input_mask=True,
use_labels=True,
vocab_size=33,
output_vocab_size=33,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=2,
intermediate_size=15,
dropout=0.1,
window_size=256,
initializer_range=0.02,
n_codes_total=8, # for BarkFineModel
n_codes_given=1, # for BarkFineModel
config_class=None,
model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.vocab_size = vocab_size
self.output_vocab_size = output_vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout = dropout
self.window_size = window_size
self.initializer_range = initializer_range
self.bos_token_id = output_vocab_size - 1
self.eos_token_id = output_vocab_size - 1
self.pad_token_id = output_vocab_size - 1
self.n_codes_total = n_codes_total
self.n_codes_given = n_codes_given
self.is_encoder_decoder = False
self.config_class = config_class
self.model_class = model_class
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length, self.n_codes_total], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
# randint between self.n_codes_given - 1 and self.n_codes_total - 1
codebook_idx = ids_tensor((1,), self.n_codes_total - self.n_codes_given).item() + self.n_codes_given
inputs_dict = {
"codebook_idx": codebook_idx,
"input_ids": input_ids,
"head_mask": head_mask,
"attention_mask": input_mask,
}
return config, inputs_dict
def get_config(self):
return self.config_class(
vocab_size=self.vocab_size,
output_vocab_size=self.output_vocab_size,
hidden_size=self.hidden_size,
num_layers=self.num_hidden_layers,
num_heads=self.num_attention_heads,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
window_size=self.window_size,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = self.model_class(config=config).to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["logits"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"logits"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
# test no attention_mask works
outputs = model(input_ids, use_cache=True)
_, past_key_values = outputs.to_tuple()
output_from_no_past = model(next_input_ids)["logits"]
output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"]
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
@require_torch
class BarkSemanticModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (BarkSemanticModel,) if is_torch_available() else ()
all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_model_parallel = False
# no model_parallel for now
test_resize_embeddings = True
def setUp(self):
self.model_tester = BarkSemanticModelTester(
self, config_class=BarkSemanticConfig, model_class=BarkSemanticModel
)
self.config_tester = ConfigTester(self, config_class=BarkSemanticConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_ids = inputs["input_ids"]
del inputs["input_ids"]
wte = model.get_input_embeddings()
inputs["input_embeds"] = wte(input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = self.all_generative_model_classes[0](config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
@require_torch
class BarkCoarseModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
# Same tester as BarkSemanticModelTest, except for model_class and config_class
all_model_classes = (BarkCoarseModel,) if is_torch_available() else ()
all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_missing_keys = False
test_pruning = False
test_model_parallel = False
# no model_parallel for now
test_resize_embeddings = True
def setUp(self):
self.model_tester = BarkCoarseModelTester(self, config_class=BarkCoarseConfig, model_class=BarkCoarseModel)
self.config_tester = ConfigTester(self, config_class=BarkCoarseConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_ids = inputs["input_ids"]
del inputs["input_ids"]
wte = model.get_input_embeddings()
inputs["input_embeds"] = wte(input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = self.all_generative_model_classes[0](config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
@require_torch
class BarkFineModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (BarkFineModel,) if is_torch_available() else ()
is_encoder_decoder = False
fx_compatible = False
test_missing_keys = False
test_pruning = False
# no model_parallel for now
test_model_parallel = False
# torchscript disabled for now because forward with an int
test_torchscript = False
test_resize_embeddings = True
def setUp(self):
self.model_tester = BarkFineModelTester(self, config_class=BarkFineConfig, model_class=BarkFineModel)
self.config_tester = ConfigTester(self, config_class=BarkFineConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_ids = inputs["input_ids"]
del inputs["input_ids"]
wte = model.get_input_embeddings()[inputs_dict["codebook_idx"]]
inputs["input_embeds"] = wte(input_ids[:, :, inputs_dict["codebook_idx"]])
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
# take first codebook channel
model = self.all_model_classes[0](config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
# toy generation_configs
semantic_generation_config = BarkSemanticGenerationConfig(semantic_vocab_size=0)
coarse_generation_config = BarkCoarseGenerationConfig(n_coarse_codebooks=config.n_codes_given)
fine_generation_config = BarkFineGenerationConfig(
max_fine_history_length=config.block_size // 2,
max_fine_input_length=config.block_size,
n_fine_codebooks=config.n_codes_total,
)
codebook_size = config.vocab_size - 1
model.generate(
input_ids,
history_prompt=None,
temperature=None,
semantic_generation_config=semantic_generation_config,
coarse_generation_config=coarse_generation_config,
fine_generation_config=fine_generation_config,
codebook_size=codebook_size,
)
model.generate(
input_ids,
history_prompt=None,
temperature=0.7,
semantic_generation_config=semantic_generation_config,
coarse_generation_config=coarse_generation_config,
fine_generation_config=fine_generation_config,
codebook_size=codebook_size,
)
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["codebook_idx", "input_ids"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_model_common_attributes(self):
# one embedding layer per codebook
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings()[0], (torch.nn.Embedding))
model.set_input_embeddings(
torch.nn.ModuleList([torch.nn.Embedding(10, 10) for _ in range(config.n_codes_total)])
)
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x[0], torch.nn.Linear))
def test_resize_tokens_embeddings(self):
# resizing tokens_embeddings of a ModuleList
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model.to(torch_device)
if self.model_tester.is_training is False:
model.eval()
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed_list = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings_list = [model_embed.weight.clone() for model_embed in model_embed_list]
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed_list = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix for each codebook
for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list):
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed_list = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list):
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
# only check for the first embedding matrix
models_equal = True
for p1, p2 in zip(cloned_embeddings_list[0], model_embed_list[0].weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_resize_embeddings_untied(self):
# resizing tokens_embeddings of a ModuleList
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
original_config.tie_word_embeddings = False
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config).to(torch_device)
# if no output embeddings -> leave test
if model.get_output_embeddings() is None:
continue
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_vocab_size = config.vocab_size
model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
output_embeds_list = model.get_output_embeddings()
for output_embeds in output_embeds_list:
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
output_embeds_list = model.get_output_embeddings()
for output_embeds in output_embeds_list:
self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
# Check bias if present
if output_embeds.bias is not None:
self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
# Input ids should be clamped to the maximum size of the vocabulary
inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1)
# Check that the model can still do a forward pass successfully (every parameter should be resized)
model(**self._prepare_for_class(inputs_dict, model_class))
@require_torch
class BarkModelIntegrationTests(unittest.TestCase):
@cached_property
def model(self):
return BarkModel.from_pretrained("ylacombe/bark-large").to(torch_device)
@cached_property
def processor(self):
return BarkProcessor.from_pretrained("ylacombe/bark-large")
@cached_property
def inputs(self):
input_ids = self.processor("In the light of the moon, a little egg lay on a leaf", voice_preset="en_speaker_6")
input_ids = input_ids.to(torch_device)
return input_ids
@cached_property
def semantic_generation_config(self):
semantic_generation_config = BarkSemanticGenerationConfig(**self.model.generation_config.semantic_config)
return semantic_generation_config
@cached_property
def coarse_generation_config(self):
coarse_generation_config = BarkCoarseGenerationConfig(**self.model.generation_config.coarse_acoustics_config)
return coarse_generation_config
@cached_property
def fine_generation_config(self):
fine_generation_config = BarkFineGenerationConfig(**self.model.generation_config.fine_acoustics_config)
return fine_generation_config
@slow
def test_generate_semantic(self):
input_ids = self.inputs
# fmt: off
# check first ids
expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,]
# fmt: on
# greedy decoding
with torch.no_grad():
output_ids = self.model.semantic.generate(
**input_ids,
do_sample=False,
semantic_generation_config=self.semantic_generation_config,
)
self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids)
@slow
def test_generate_coarse(self):
input_ids = self.inputs
history_prompt = input_ids["history_prompt"]
# fmt: off
# check first ids
expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ]
# fmt: on
with torch.no_grad():
output_ids = self.model.semantic.generate(
**input_ids,
do_sample=False,
semantic_generation_config=self.semantic_generation_config,
)
output_ids = self.model.coarse_acoustics.generate(
output_ids,
history_prompt=history_prompt,
do_sample=False,
semantic_generation_config=self.semantic_generation_config,
coarse_generation_config=self.coarse_generation_config,
codebook_size=self.model.generation_config.codebook_size,
)
self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids)
@slow
def test_generate_fine(self):
input_ids = self.inputs
history_prompt = input_ids["history_prompt"]
# fmt: off
expected_output_ids = [
[1018, 651, 857, 642, 312, 531, 474, 524, 524, 776,],
[367, 394, 596, 342, 504, 492, 27, 27, 822, 822,],
[961, 955, 221, 955, 955, 686, 939, 939, 479, 176,],
[638, 365, 218, 944, 853, 363, 639, 22, 884, 456,],
[302, 912, 524, 38, 174, 209, 879, 23, 910, 227,],
[440, 673, 861, 666, 372, 558, 49, 172, 232, 342,],
[244, 358, 123, 356, 586, 520, 499, 877, 542, 637,],
[806, 685, 905, 848, 803, 810, 921, 208, 625, 203,],
]
# fmt: on
with torch.no_grad():
output_ids = self.model.semantic.generate(
**input_ids,
do_sample=False,
semantic_generation_config=self.semantic_generation_config,
)
output_ids = self.model.coarse_acoustics.generate(
output_ids,
history_prompt=history_prompt,
do_sample=False,
semantic_generation_config=self.semantic_generation_config,
coarse_generation_config=self.coarse_generation_config,
codebook_size=self.model.generation_config.codebook_size,
)
# greedy decoding
output_ids = self.model.fine_acoustics.generate(
output_ids,
history_prompt=history_prompt,
temperature=None,
semantic_generation_config=self.semantic_generation_config,
coarse_generation_config=self.coarse_generation_config,
fine_generation_config=self.fine_generation_config,
codebook_size=self.model.generation_config.codebook_size,
)
self.assertListEqual(output_ids[0, :, : len(expected_output_ids[0])].tolist(), expected_output_ids)
@slow
def test_generate_end_to_end(self):
input_ids = self.inputs
with torch.no_grad():
self.model.generate(**input_ids)
self.model.generate(**{key: val for (key, val) in input_ids.items() if key != "history_prompt"})
@slow
def test_generate_end_to_end_with_args(self):
input_ids = self.inputs
with torch.no_grad():
self.model.generate(**input_ids, do_sample=True, temperature=0.6, penalty_alpha=0.6)
self.model.generate(**input_ids, do_sample=True, temperature=0.6, num_beams=4)
@slow
def test_generate_end_to_end_with_sub_models_args(self):
input_ids = self.inputs
with torch.no_grad():
self.model.generate(**input_ids, do_sample=False, coarse_do_sample=True, coarse_temperature=0.7)
self.model.generate(
**input_ids, do_sample=False, coarse_do_sample=True, coarse_temperature=0.7, fine_temperature=0.3
)
self.model.generate(
**input_ids,
do_sample=True,
temperature=0.6,
penalty_alpha=0.6,
semantic_temperature=0.9,
coarse_temperature=0.2,
fine_temperature=0.1,
)
| 39,190 | 38.507056 | 144 | py |
transformers | transformers-main/tests/models/bark/test_processor_bark.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class BarkProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "ylacombe/bark-small"
self.tmpdirname = tempfile.mkdtemp()
self.voice_preset = "en_speaker_1"
self.input_string = "This is a test string"
self.speaker_embeddings_dict_path = "speaker_embeddings_path.json"
self.speaker_embeddings_directory = "speaker_embeddings"
def get_tokenizer(self, **kwargs):
return AutoTokenizer.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
processor = BarkProcessor(tokenizer=tokenizer)
processor.save_pretrained(self.tmpdirname)
processor = BarkProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
@slow
def test_save_load_pretrained_additional_features(self):
processor = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint,
speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,
)
processor.save_pretrained(
self.tmpdirname,
speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,
speaker_embeddings_directory=self.speaker_embeddings_directory,
)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
processor = BarkProcessor.from_pretrained(
self.tmpdirname,
self.speaker_embeddings_dict_path,
bos_token="(BOS)",
eos_token="(EOS)",
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
def test_speaker_embeddings(self):
processor = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint,
speaker_embeddings_dict_path=self.speaker_embeddings_dict_path,
)
seq_len = 35
nb_codebooks_coarse = 2
nb_codebooks_total = 8
voice_preset = {
"semantic_prompt": np.ones(seq_len),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
"fine_prompt": np.ones((nb_codebooks_total, seq_len)),
}
# test providing already loaded voice_preset
inputs = processor(text=self.input_string, voice_preset=voice_preset)
processed_voice_preset = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(key, np.array([])).tolist())
# test loading voice preset from npz file
tmpfilename = os.path.join(self.tmpdirname, "file.npz")
np.savez(tmpfilename, **voice_preset)
inputs = processor(text=self.input_string, voice_preset=tmpfilename)
processed_voice_preset = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(key, np.array([])).tolist())
# test loading voice preset from the hub
inputs = processor(text=self.input_string, voice_preset=self.voice_preset)
def test_tokenizer(self):
tokenizer = self.get_tokenizer()
processor = BarkProcessor(tokenizer=tokenizer)
encoded_processor = processor(text=self.input_string)
encoded_tok = tokenizer(
self.input_string,
padding="max_length",
max_length=256,
add_special_tokens=False,
return_attention_mask=True,
return_token_type_ids=False,
)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key].squeeze().tolist())
| 4,660 | 35.414063 | 116 | py |
transformers | transformers-main/tests/models/wav2vec2/test_feature_extraction_wav2vec2.py | # coding=utf-8
# Copyright 2021 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2Config, Wav2Vec2FeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class Wav2Vec2FeatureExtractionTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=1,
padding_value=0.0,
sampling_rate=16000,
return_attention_mask=True,
do_normalize=True,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.padding_value = padding_value
self.sampling_rate = sampling_rate
self.return_attention_mask = return_attention_mask
self.do_normalize = do_normalize
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
def _flatten(list_of_lists):
return list(itertools.chain(*list_of_lists))
if equal_length:
speech_inputs = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
speech_inputs = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
speech_inputs = [np.asarray(x) for x in speech_inputs]
return speech_inputs
class Wav2Vec2FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = Wav2Vec2FeatureExtractor
def setUp(self):
self.feat_extract_tester = Wav2Vec2FeatureExtractionTester(self)
def _check_zero_mean_unit_variance(self, input_vector):
self.assertTrue(np.all(np.mean(input_vector, axis=0) < 1e-3))
self.assertTrue(np.all(np.abs(np.var(input_vector, axis=0) - 1) < 1e-3))
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test not batched input
encoded_sequences_1 = feat_extract(speech_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = feat_extract(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_zero_mean_unit_variance_normalization_np(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, padding=padding, max_length=max_length, return_tensors="np")
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self.assertTrue(input_values[0][800:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[1][:1000])
self.assertTrue(input_values[0][1000:].sum() < 1e-6)
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
lengths = range(800, 1400, 200)
speech_inputs = [floats_list((1, x))[0] for x in lengths]
paddings = ["longest", "max_length", "do_not_pad"]
max_lengths = [None, 1600, None]
for max_length, padding in zip(max_lengths, paddings):
processed = feat_extract(speech_inputs, max_length=max_length, padding=padding)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800])
self._check_zero_mean_unit_variance(input_values[1][:1000])
self._check_zero_mean_unit_variance(input_values[2][:1200])
def test_zero_mean_unit_variance_normalization_trunc_np_max_length(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="max_length", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1])
self._check_zero_mean_unit_variance(input_values[2])
def test_zero_mean_unit_variance_normalization_trunc_np_longest(self):
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=1000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000))
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = feat_extract(
speech_inputs, truncation=True, max_length=2000, padding="longest", return_tensors="np"
)
input_values = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800])
self._check_zero_mean_unit_variance(input_values[1, :1000])
self._check_zero_mean_unit_variance(input_values[2])
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200))
@require_torch
def test_double_precision_pad(self):
import torch
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_speech_inputs = np.random.rand(100).astype(np.float64)
py_speech_inputs = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_values.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_values.dtype == torch.float32)
@slow
@require_torch
def test_pretrained_checkpoints_are_set_correctly(self):
# this test makes sure that models that are using
# group norm don't have their feature extractor return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
config = Wav2Vec2Config.from_pretrained(model_id)
feat_extract = Wav2Vec2FeatureExtractor.from_pretrained(model_id)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(feat_extract.return_attention_mask, config.feat_extract_norm == "layer")
| 10,319 | 43.291845 | 113 | py |
transformers | transformers-main/tests/models/wav2vec2/test_tokenization_wav2vec2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the Wav2Vec2 tokenizer."""
import inspect
import json
import os
import random
import shutil
import tempfile
import unittest
import numpy as np
from transformers import (
WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
Wav2Vec2Config,
Wav2Vec2CTCTokenizer,
Wav2Vec2Tokenizer,
)
from transformers.models.wav2vec2.tokenization_wav2vec2 import VOCAB_FILES_NAMES, Wav2Vec2CTCTokenizerOutput
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
global_rng = random.Random()
def floats_list(shape, scale=1.0, rng=None, name=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = global_rng
values = []
for batch_idx in range(shape[0]):
values.append([])
for _ in range(shape[1]):
values[-1].append(rng.random() * scale)
return values
class Wav2Vec2TokenizerTest(unittest.TestCase):
tokenizer_class = Wav2Vec2Tokenizer
def setUp(self):
super().setUp()
vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
self.tmpdirname = tempfile.mkdtemp()
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return Wav2Vec2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
def test_tokenizer_decode(self):
# TODO(PVP) - change to facebook
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
]
tokens = tokenizer.decode(sample_ids[0])
batch_tokens = tokenizer.batch_decode(sample_ids)
self.assertEqual(tokens, batch_tokens[0])
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
def test_tokenizer_decode_special(self):
# TODO(PVP) - change to facebook
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
]
sample_ids_2 = [
[11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
[
24,
22,
5,
tokenizer.pad_token_id,
tokenizer.pad_token_id,
tokenizer.pad_token_id,
tokenizer.word_delimiter_token_id,
24,
22,
5,
77,
tokenizer.word_delimiter_token_id,
],
]
batch_tokens = tokenizer.batch_decode(sample_ids)
batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
self.assertEqual(batch_tokens, batch_tokens_2)
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
def test_tokenizer_decode_added_tokens(self):
tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
tokenizer.add_tokens(["!", "?"])
tokenizer.add_special_tokens({"cls_token": "$$$"})
sample_ids = [
[
11,
5,
15,
tokenizer.pad_token_id,
15,
8,
98,
32,
32,
33,
tokenizer.word_delimiter_token_id,
32,
32,
33,
34,
34,
],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34],
]
batch_tokens = tokenizer.batch_decode(sample_ids)
self.assertEqual(batch_tokens, ["HELLO<unk>!?!?$$$", "BYE BYE<unk>$$$"])
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
tokenizer = self.get_tokenizer()
# create three inputs of length 800, 1000, and 1200
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
# Test not batched input
encoded_sequences_1 = tokenizer(speech_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = tokenizer(np_speech_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = tokenizer(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = tokenizer(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test 2-D numpy arrays are batched.
speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
np_speech_inputs = np.asarray(speech_inputs)
encoded_sequences_1 = tokenizer(speech_inputs, return_tensors="np").input_values
encoded_sequences_2 = tokenizer(np_speech_inputs, return_tensors="np").input_values
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
def test_padding(self, max_length=50):
def _input_values_have_equal_length(input_values):
length = len(input_values[0])
for input_values_slice in input_values[1:]:
if len(input_values_slice) != length:
return False
return True
def _input_values_are_equal(input_values_1, input_values_2):
if len(input_values_1) != len(input_values_2):
return False
for input_values_slice_1, input_values_slice_2 in zip(input_values_1, input_values_2):
if not np.allclose(np.asarray(input_values_slice_1), np.asarray(input_values_slice_2), atol=1e-3):
return False
return True
tokenizer = self.get_tokenizer()
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
input_values_1 = tokenizer(speech_inputs).input_values
input_values_2 = tokenizer(speech_inputs, padding="longest").input_values
input_values_3 = tokenizer(speech_inputs, padding="longest", max_length=1600).input_values
self.assertFalse(_input_values_have_equal_length(input_values_1))
self.assertTrue(_input_values_have_equal_length(input_values_2))
self.assertTrue(_input_values_have_equal_length(input_values_3))
self.assertTrue(_input_values_are_equal(input_values_2, input_values_3))
self.assertTrue(len(input_values_1[0]) == 800)
self.assertTrue(len(input_values_2[0]) == 1200)
# padding should be 0.0
self.assertTrue(abs(sum(np.asarray(input_values_2[0])[800:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_2[1])[1000:])) < 1e-3)
input_values_4 = tokenizer(speech_inputs, padding="max_length").input_values
input_values_5 = tokenizer(speech_inputs, padding="max_length", max_length=1600).input_values
self.assertTrue(_input_values_are_equal(input_values_1, input_values_4))
self.assertEqual(input_values_5.shape, (3, 1600))
# padding should be 0.0
self.assertTrue(abs(sum(np.asarray(input_values_5[0])[800:1200])) < 1e-3)
input_values_6 = tokenizer(speech_inputs, pad_to_multiple_of=500).input_values
input_values_7 = tokenizer(speech_inputs, padding="longest", pad_to_multiple_of=500).input_values
input_values_8 = tokenizer(
speech_inputs, padding="max_length", pad_to_multiple_of=500, max_length=2400
).input_values
self.assertTrue(_input_values_are_equal(input_values_1, input_values_6))
self.assertEqual(input_values_7.shape, (3, 1500))
self.assertEqual(input_values_8.shape, (3, 2500))
# padding should be 0.0
self.assertTrue(abs(sum(np.asarray(input_values_7[0])[800:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_7[1])[1000:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_7[2])[1200:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_8[0])[800:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_8[1])[1000:])) < 1e-3)
self.assertTrue(abs(sum(np.asarray(input_values_8[2])[1200:])) < 1e-3)
def test_save_pretrained(self):
pretrained_name = list(self.tokenizer_class.pretrained_vocab_files_map["vocab_file"].keys())[0]
tokenizer = self.tokenizer_class.from_pretrained(pretrained_name)
tmpdirname2 = tempfile.mkdtemp()
tokenizer_files = tokenizer.save_pretrained(tmpdirname2)
self.assertSequenceEqual(
sorted(tuple(VOCAB_FILES_NAMES.values()) + ("special_tokens_map.json", "added_tokens.json")),
sorted(x.split(os.path.sep)[-1] for x in tokenizer_files),
)
# Checks everything loads correctly in the same way
tokenizer_p = self.tokenizer_class.from_pretrained(tmpdirname2)
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer.special_tokens_map:
self.assertTrue(key in tokenizer_p.special_tokens_map)
shutil.rmtree(tmpdirname2)
def test_get_vocab(self):
tokenizer = self.get_tokenizer()
vocab_dict = tokenizer.get_vocab()
self.assertIsInstance(vocab_dict, dict)
self.assertGreaterEqual(len(tokenizer), len(vocab_dict))
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
tokenizer.add_tokens(["asdfasdfasdfasdf"])
vocab = [tokenizer.convert_ids_to_tokens(i) for i in range(len(tokenizer))]
self.assertEqual(len(vocab), len(tokenizer))
def test_save_and_load_tokenizer(self):
tokenizer = self.get_tokenizer()
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_ids = [0, 1, 4, 8, 9, 0, 12]
before_tokens = tokenizer.decode(sample_ids)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.decode(sample_ids)
after_vocab = after_tokenizer.get_vocab()
self.assertEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
shutil.rmtree(tmpdirname)
tokenizer = self.get_tokenizer()
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
before_len = len(tokenizer)
sample_ids = [0, 1, 4, 8, 9, 0, 12, before_len, before_len + 1, before_len + 2]
tokenizer.add_tokens(["?", "!"])
additional_special_tokens = tokenizer.additional_special_tokens
additional_special_tokens.append("&")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.decode(sample_ids)
before_vocab = tokenizer.get_vocab()
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.decode(sample_ids)
after_vocab = after_tokenizer.get_vocab()
self.assertEqual(before_tokens, after_tokens)
self.assertDictEqual(before_vocab, after_vocab)
self.assertTrue(len(tokenizer), before_len + 3)
self.assertTrue(len(tokenizer), len(after_tokenizer))
shutil.rmtree(tmpdirname)
def test_tokenizer_slow_store_full_signature(self):
signature = inspect.signature(self.tokenizer_class.__init__)
tokenizer = self.get_tokenizer()
for parameter_name, parameter in signature.parameters.items():
if parameter.default != inspect.Parameter.empty:
self.assertIn(parameter_name, tokenizer.init_kwargs)
def test_zero_mean_unit_variance_normalization(self):
tokenizer = self.get_tokenizer(do_normalize=True)
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
processed = tokenizer(speech_inputs, padding="longest")
input_values = processed.input_values
def _check_zero_mean_unit_variance(input_vector):
self.assertTrue(np.abs(np.mean(input_vector)) < 1e-3)
self.assertTrue(np.abs(np.var(input_vector) - 1) < 1e-3)
_check_zero_mean_unit_variance(input_values[0, :800])
_check_zero_mean_unit_variance(input_values[1, :1000])
_check_zero_mean_unit_variance(input_values[2])
def test_return_attention_mask(self):
speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
# default case -> no attention_mask is returned
tokenizer = self.get_tokenizer()
processed = tokenizer(speech_inputs)
self.assertNotIn("attention_mask", processed)
# wav2vec2-lv60 -> return attention_mask
tokenizer = self.get_tokenizer(return_attention_mask=True)
processed = tokenizer(speech_inputs, padding="longest")
self.assertIn("attention_mask", processed)
self.assertListEqual(list(processed.attention_mask.shape), list(processed.input_values.shape))
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), [800, 1000, 1200])
@slow
@require_torch
def test_pretrained_checkpoints_are_set_correctly(self):
# this test makes sure that models that are using
# group norm don't have their tokenizer return the
# attention_mask
for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
config = Wav2Vec2Config.from_pretrained(model_id)
tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_id)
# only "layer" feature extraction norm should make use of
# attention_mask
self.assertEqual(tokenizer.return_attention_mask, config.feat_extract_norm == "layer")
class Wav2Vec2CTCTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = Wav2Vec2CTCTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab = "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
self.tmpdirname = tempfile.mkdtemp()
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
def get_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return Wav2Vec2CTCTokenizer.from_pretrained(self.tmpdirname, **kwargs)
def test_tokenizer_add_token_chars(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
# check adding a single token
tokenizer.add_tokens("x")
token_ids = tokenizer("C x A").input_ids
self.assertEqual(token_ids, [19, 4, 32, 4, 7])
tokenizer.add_tokens(["a", "b", "c"])
token_ids = tokenizer("C a A c").input_ids
self.assertEqual(token_ids, [19, 4, 33, 4, 7, 4, 35])
tokenizer.add_tokens(["a", "b", "c"])
token_ids = tokenizer("CaA c").input_ids
self.assertEqual(token_ids, [19, 33, 7, 4, 35])
def test_tokenizer_add_token_words(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
# check adding a single token
tokenizer.add_tokens("xxx")
token_ids = tokenizer("C xxx A B").input_ids
self.assertEqual(token_ids, [19, 4, 32, 4, 7, 4, 24])
tokenizer.add_tokens(["aaa", "bbb", "ccc"])
token_ids = tokenizer("C aaa A ccc B B").input_ids
self.assertEqual(token_ids, [19, 4, 33, 4, 7, 4, 35, 4, 24, 4, 24])
tokenizer.add_tokens(["aaa", "bbb", "ccc"])
token_ids = tokenizer("CaaaA ccc B B").input_ids
self.assertEqual(token_ids, [19, 33, 7, 4, 35, 4, 24, 4, 24])
def test_tokenizer_decode(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
]
tokens = tokenizer.decode(sample_ids[0])
batch_tokens = tokenizer.batch_decode(sample_ids)
self.assertEqual(tokens, batch_tokens[0])
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
def test_tokenizer_decode_special(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
# fmt: off
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77],
]
sample_ids_2 = [
[11, 5, 5, 5, 5, 5, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98],
[24, 22, 5, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.word_delimiter_token_id],
]
# fmt: on
batch_tokens = tokenizer.batch_decode(sample_ids)
batch_tokens_2 = tokenizer.batch_decode(sample_ids_2)
self.assertEqual(batch_tokens, batch_tokens_2)
self.assertEqual(batch_tokens, ["HELLO<unk>", "BYE BYE<unk>"])
def test_tokenizer_decode_added_tokens(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
tokenizer.add_tokens(["!", "?"])
tokenizer.add_special_tokens({"cls_token": "$$$"})
# fmt: off
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 32, 32, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, 24, 22, 5, 77, tokenizer.pad_token_id, 34, 34],
]
# fmt: on
batch_tokens = tokenizer.batch_decode(sample_ids)
self.assertEqual(batch_tokens, ["HELLO<unk>!?!?$$$", "BYE BYE<unk>$$$"])
def test_special_characters_in_vocab(self):
sent = "ʈʰ æ æ̃ ˧ kʰ"
vocab_dict = {k: v for v, k in enumerate(set(sent.split()))}
vocab_file = os.path.join(self.tmpdirname, "vocab_special.json")
with open(vocab_file, "w") as f:
json.dump(vocab_dict, f)
tokenizer = Wav2Vec2CTCTokenizer(vocab_file)
expected_sent = tokenizer.decode(tokenizer(sent).input_ids, spaces_between_special_tokens=True)
self.assertEqual(sent, expected_sent)
tokenizer.save_pretrained(os.path.join(self.tmpdirname, "special_tokenizer"))
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(os.path.join(self.tmpdirname, "special_tokenizer"))
expected_sent = tokenizer.decode(tokenizer(sent).input_ids, spaces_between_special_tokens=True)
self.assertEqual(sent, expected_sent)
@staticmethod
def get_from_offsets(offsets, key):
retrieved_list = [d[key] for d in offsets]
return retrieved_list
def test_offsets(self):
tokenizer = self.get_tokenizer()
# fmt: off
# HEEEEE||LLL<pad>LO<unk> => HE LLO<unk>
# 1H + 5E + 2| + 3L + 1<pad> + 1L + 1O + 1<unk>
sample_ids = [11, 5, 5, 5, 5, 5, 4, 4, 15, 15, 15, tokenizer.pad_token_id, 15, 8, 98]
# fmt: on
outputs_char = tokenizer.decode(sample_ids, output_char_offsets=True)
# check Wav2Vec2CTCTokenizerOutput keys for char
self.assertEqual(len(outputs_char.keys()), 2)
self.assertTrue("text" in outputs_char)
self.assertTrue("char_offsets" in outputs_char)
self.assertTrue(isinstance(outputs_char, Wav2Vec2CTCTokenizerOutput))
outputs_word = tokenizer.decode(sample_ids, output_word_offsets=True)
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs_word.keys()), 2)
self.assertTrue("text" in outputs_word)
self.assertTrue("word_offsets" in outputs_word)
self.assertTrue(isinstance(outputs_word, Wav2Vec2CTCTokenizerOutput))
outputs = tokenizer.decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
# check Wav2Vec2CTCTokenizerOutput keys for both
self.assertEqual(len(outputs.keys()), 3)
self.assertTrue("text" in outputs)
self.assertTrue("char_offsets" in outputs)
self.assertTrue("word_offsets" in outputs)
self.assertTrue(isinstance(outputs, Wav2Vec2CTCTokenizerOutput))
# check that order of chars is correct and identical for both outputs
self.assertEqual("".join(self.get_from_offsets(outputs["char_offsets"], "char")), outputs.text)
self.assertEqual(
self.get_from_offsets(outputs["char_offsets"], "char"), ["H", "E", " ", "L", "L", "O", "<unk>"]
)
self.assertListEqual(
self.get_from_offsets(outputs["char_offsets"], "char"),
self.get_from_offsets(outputs_char["char_offsets"], "char"),
)
# check that order of words is correct and identical to both outputs
self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"], "word")), outputs.text)
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "word"), ["HE", "LLO<unk>"])
self.assertListEqual(
self.get_from_offsets(outputs["word_offsets"], "word"),
self.get_from_offsets(outputs_word["word_offsets"], "word"),
)
# check that offsets are actually correct for char
# 0 is H, 1 is E, 6 is | (" "), 8 is 1st L, 12 is 2nd L, 13 is O, 14 is <unk>
self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "start_offset"), [0, 1, 6, 8, 12, 13, 14])
# 1 is H, 6 is E, 8 is | (" "), 11 is 1st L (note due to <pad>
# different begin of 2nd L), 13 is 2nd L, 14 is O, 15 is <unk>
self.assertListEqual(self.get_from_offsets(outputs["char_offsets"], "end_offset"), [1, 6, 8, 11, 13, 14, 15])
# check that offsets are actually correct for word
# H is at 1st position of first word, first L is at 8th position of second word
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "start_offset"), [0, 8])
# last E is at 6th position of first word, first L is at last (15th) position of second word
self.assertListEqual(self.get_from_offsets(outputs["word_offsets"], "end_offset"), [6, 15])
def test_word_offsets_from_char_offsets(self):
tokenizer = self.get_tokenizer()
char_offsets = [
{"char": "H", "start_offset": 0, "end_offset": 1},
{"char": "I", "start_offset": 1, "end_offset": 2},
{"char": " ", "start_offset": 2, "end_offset": 3},
{"char": "L", "start_offset": 3, "end_offset": 4},
{"char": "I", "start_offset": 4, "end_offset": 5},
]
word_offsets = tokenizer._get_word_offsets(char_offsets, tokenizer.replace_word_delimiter_char)
self.assertEqual(
word_offsets,
[{"word": "HI", "start_offset": 0, "end_offset": 2}, {"word": "LI", "start_offset": 3, "end_offset": 5}],
)
# Double spaces don't get counted
char_offsets = [
{"char": " ", "start_offset": 0, "end_offset": 1},
{"char": "H", "start_offset": 1, "end_offset": 2},
{"char": "I", "start_offset": 2, "end_offset": 3},
{"char": " ", "start_offset": 3, "end_offset": 4},
{"char": " ", "start_offset": 4, "end_offset": 5},
{"char": "L", "start_offset": 5, "end_offset": 6},
{"char": "I", "start_offset": 6, "end_offset": 7},
{"char": "I", "start_offset": 7, "end_offset": 8},
{"char": " ", "start_offset": 8, "end_offset": 9},
{"char": " ", "start_offset": 9, "end_offset": 10},
]
word_offsets = tokenizer._get_word_offsets(char_offsets, tokenizer.replace_word_delimiter_char)
self.assertEqual(
word_offsets,
[{"word": "HI", "start_offset": 1, "end_offset": 3}, {"word": "LII", "start_offset": 5, "end_offset": 8}],
)
def test_offsets_batch(self):
tokenizer = self.get_tokenizer()
def check_list_tuples_equal(outputs_batch, outputs_list):
self.assertTrue(isinstance(outputs_batch, Wav2Vec2CTCTokenizerOutput))
self.assertTrue(isinstance(outputs_list[0], Wav2Vec2CTCTokenizerOutput))
# transform list to ModelOutput
outputs_batch_2 = Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in outputs_list] for k in outputs_list[0]})
self.assertListEqual(outputs_batch["text"], outputs_batch_2["text"])
def recursive_check(list_or_dict_1, list_or_dict_2):
if isinstance(list_or_dict_1, list):
[recursive_check(l1, l2) for l1, l2 in zip(list_or_dict_1, list_or_dict_2)]
self.assertEqual(list_or_dict_1, list_or_dict_2)
if "char_offsets" in outputs_batch:
recursive_check(outputs_batch["char_offsets"], outputs_batch_2["char_offsets"])
if "word_offsets" in outputs_batch:
recursive_check(outputs_batch["word_offsets"], outputs_batch_2["word_offsets"])
# fmt: off
sample_ids = [
[11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
[24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
]
# fmt: on
# We assume that `decode` works as expected. All we will check now is
# the output type is correct and the output is identical to `decode`
# char
outputs_char_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True)
outputs_char = [tokenizer.decode(ids, output_char_offsets=True) for ids in sample_ids]
check_list_tuples_equal(outputs_char_batch, outputs_char)
# word
outputs_word_batch = tokenizer.batch_decode(sample_ids, output_word_offsets=True)
outputs_word = [tokenizer.decode(ids, output_word_offsets=True) for ids in sample_ids]
check_list_tuples_equal(outputs_word_batch, outputs_word)
# both
outputs_batch = tokenizer.batch_decode(sample_ids, output_char_offsets=True, output_word_offsets=True)
outputs = [tokenizer.decode(ids, output_word_offsets=True, output_char_offsets=True) for ids in sample_ids]
check_list_tuples_equal(outputs_batch, outputs)
def test_offsets_integration(self):
tokenizer = self.tokenizer_class.from_pretrained("facebook/wav2vec2-base-960h")
# pred_ids correspond to the following code
# ```
# from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC
# from datasets import load_dataset
# import datasets
# import torch
# model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h")
# feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
#
# ds = load_dataset("common_voice", "en", split="train", streaming=True)
# ds = ds.cast_column("audio", datasets.Audio(sampling_rate=16_000))
# ds_iter = iter(ds)
# sample = next(ds_iter)
#
# input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values
# logits = model(input_values).logits
# pred_ids = torch.argmax(logits, axis=-1).cpu().tolist()
# ```
# fmt: off
pred_ids = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 11, 0, 0, 0, 22, 0, 0, 4, 4, 4, 14, 0, 0, 0, 0, 0, 8, 8, 0, 5, 5, 0, 12, 0, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 0, 0, 10, 0, 0, 0, 15, 0, 0, 10, 0, 0, 0, 12, 0, 0, 0, 0, 0, 7, 0, 9, 0, 0, 14, 0, 0, 0, 13, 0, 7, 0, 0, 4, 4, 0, 15, 8, 8, 0, 0, 8, 0, 26, 0, 0, 4, 4, 0, 0, 15, 0, 0, 0, 0, 0, 0, 10, 0, 26, 5, 5, 0, 4, 4, 0, 0, 12, 11, 0, 0, 5, 4, 4, 4, 0, 18, 0, 0, 0, 7, 9, 9, 0, 6, 0, 12, 12, 4, 4, 0, 6, 0, 0, 8, 0, 4, 4, 4, 0, 19, 0, 0, 8, 9, 9, 0, 0, 0, 0, 12, 12, 0, 0, 0, 0, 0, 0, 0, 16, 16, 0, 0, 17, 5, 5, 5, 0, 4, 4, 4, 0, 0, 29, 29, 0, 0, 0, 0, 8, 11, 0, 9, 9, 0, 0, 0, 4, 4, 0, 12, 12, 0, 0, 0, 9, 0, 0, 0, 0, 0, 8, 18, 0, 0, 0, 4, 4, 0, 0, 8, 9, 0, 4, 4, 0, 6, 11, 5, 0, 4, 4, 0, 13, 13, 0, 0, 0, 10, 0, 0, 25, 0, 0, 6, 0, 4, 4, 0, 0, 0, 0, 7, 0, 0, 23, 0, 0, 4, 4, 0, 0, 0, 6, 11, 0, 5, 4, 4, 18, 0, 0, 0, 0, 0, 0, 7, 15, 0, 0, 0, 15, 15, 0, 4, 4, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
# wav2vec2-base downsamples input audio by a factor of 320
# sampling rate for wav2vec2-base is 16_000
time_offset_wav2vec2_base = 320 / 16_000
expected_char_time_stamps_text = ['W', 'H', 'Y', ' ', 'D', 'O', 'E', 'S', ' ', 'M', 'I', 'L', 'I', 'S', 'A', 'N', 'D', 'R', 'A', ' ', 'L', 'O', 'O', 'K', ' ', 'L', 'I', 'K', 'E', ' ', 'S', 'H', 'E', ' ', 'W', 'A', 'N', 'T', 'S', ' ', 'T', 'O', ' ', 'C', 'O', 'N', 'S', 'U', 'M', 'E', ' ', 'J', 'O', 'H', 'N', ' ', 'S', 'N', 'O', 'W', ' ', 'O', 'N', ' ', 'T', 'H', 'E', ' ', 'R', 'I', 'V', 'T', ' ', 'A', 'P', ' ', 'T', 'H', 'E', ' ', 'W', 'A', 'L', 'L', ' ']
expected_char_time_stamps_start = [1.42, 1.44, 1.52, 1.58, 1.64, 1.76, 1.82, 1.88, 1.92, 2.26, 2.32, 2.4, 2.46, 2.54, 2.66, 2.7, 2.76, 2.84, 2.88, 2.94, 3.0, 3.02, 3.1, 3.14, 3.2, 3.28, 3.42, 3.46, 3.48, 3.54, 3.62, 3.64, 3.7, 3.72, 3.8, 3.88, 3.9, 3.96, 4.0, 4.04, 4.1, 4.16, 4.2, 4.28, 4.34, 4.36, 4.48, 4.66, 4.74, 4.76, 4.84, 4.94, 5.06, 5.08, 5.12, 5.22, 5.28, 5.38, 5.5, 5.52, 5.6, 5.68, 5.7, 5.74, 5.8, 5.82, 5.84, 5.88, 5.94, 6.04, 6.1, 6.16, 6.2, 6.32, 6.38, 6.44, 6.54, 6.56, 6.6, 6.62, 6.66, 6.8, 6.82, 6.9, 6.96]
expected_char_time_stamps_end = [1.44, 1.46, 1.54, 1.64, 1.66, 1.8, 1.86, 1.9, 2.06, 2.28, 2.34, 2.42, 2.48, 2.56, 2.68, 2.72, 2.78, 2.86, 2.9, 2.98, 3.02, 3.06, 3.12, 3.16, 3.24, 3.3, 3.44, 3.48, 3.52, 3.58, 3.64, 3.66, 3.72, 3.78, 3.82, 3.9, 3.94, 3.98, 4.04, 4.08, 4.12, 4.18, 4.26, 4.3, 4.36, 4.4, 4.52, 4.7, 4.76, 4.82, 4.9, 4.98, 5.08, 5.1, 5.16, 5.26, 5.32, 5.4, 5.52, 5.54, 5.64, 5.7, 5.72, 5.78, 5.82, 5.84, 5.86, 5.92, 5.98, 6.06, 6.12, 6.18, 6.24, 6.34, 6.4, 6.48, 6.56, 6.58, 6.62, 6.66, 6.68, 6.82, 6.84, 6.94, 7.02]
expected_word_time_stamps_text = ['WHY', 'DOES', 'MILISANDRA', 'LOOK', 'LIKE', 'SHE', 'WANTS', 'TO', 'CONSUME', 'JOHN', 'SNOW', 'ON', 'THE', 'RIVT', 'AP', 'THE', 'WALL']
expected_word_time_stamps_start = [1.42, 1.64, 2.26, 3.0, 3.28, 3.62, 3.8, 4.1, 4.28, 4.94, 5.28, 5.68, 5.8, 5.94, 6.32, 6.54, 6.66]
expected_word_time_stamps_end = [1.54, 1.9, 2.9, 3.16, 3.52, 3.72, 4.04, 4.18, 4.82, 5.16, 5.54, 5.72, 5.86, 6.18, 6.4, 6.62, 6.94]
# fmt: on
output = tokenizer.batch_decode(pred_ids, output_char_offsets=True, output_word_offsets=True)
char_offsets_text = self.get_from_offsets(output["char_offsets"][0], "char")
char_offsets_start = self.get_from_offsets(output["char_offsets"][0], "start_offset")
char_offsets_end = self.get_from_offsets(output["char_offsets"][0], "end_offset")
word_offsets_text = self.get_from_offsets(output["word_offsets"][0], "word")
word_offsets_start = self.get_from_offsets(output["word_offsets"][0], "start_offset")
word_offsets_end = self.get_from_offsets(output["word_offsets"][0], "end_offset")
# let's transform offsets to time stamps in seconds
char_time_stamps_start = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_start]
char_time_stamps_end = [round(c * time_offset_wav2vec2_base, 2) for c in char_offsets_end]
word_time_stamps_start = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_start]
word_time_stamps_end = [round(w * time_offset_wav2vec2_base, 2) for w in word_offsets_end]
# NOTE: you can verify the above results by checking out the dataset viewer
# on https://huggingface.co/datasets/common_voice/viewer/en/train and
# downloading / playing the sample `common_voice_en_100038.mp3`. As
# you can hear the time-stamps match more or less
self.assertListEqual(expected_char_time_stamps_text, char_offsets_text)
self.assertListEqual(expected_char_time_stamps_start, char_time_stamps_start)
self.assertListEqual(expected_char_time_stamps_end, char_time_stamps_end)
self.assertListEqual(expected_word_time_stamps_text, word_offsets_text)
self.assertListEqual(expected_word_time_stamps_start, word_time_stamps_start)
self.assertListEqual(expected_word_time_stamps_end, word_time_stamps_end)
def test_pretrained_model_lists(self):
# Wav2Vec2Model has no max model length => no testing
pass
# overwrite from test_tokenization_common
def test_add_tokens_tokenizer(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
vocab_size = tokenizer.vocab_size
all_size = len(tokenizer)
self.assertNotEqual(vocab_size, 0)
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
added_toks = tokenizer.add_tokens(new_toks)
vocab_size_2 = tokenizer.vocab_size
all_size_2 = len(tokenizer)
self.assertNotEqual(vocab_size_2, 0)
self.assertEqual(vocab_size, vocab_size_2)
self.assertEqual(added_toks, len(new_toks))
self.assertEqual(all_size_2, all_size + len(new_toks))
tokens = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l", add_special_tokens=False)
self.assertGreaterEqual(len(tokens), 4)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
added_toks_2 = tokenizer.add_special_tokens(new_toks_2)
vocab_size_3 = tokenizer.vocab_size
all_size_3 = len(tokenizer)
self.assertNotEqual(vocab_size_3, 0)
self.assertEqual(vocab_size, vocab_size_3)
self.assertEqual(added_toks_2, len(new_toks_2))
self.assertEqual(all_size_3, all_size_2 + len(new_toks_2))
tokens = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l", add_special_tokens=False
)
self.assertGreaterEqual(len(tokens), 6)
self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
self.assertGreater(tokens[0], tokens[1])
self.assertGreater(tokens[-3], tokenizer.vocab_size - 1)
self.assertGreater(tokens[-3], tokens[-4])
self.assertEqual(tokens[0], tokenizer.eos_token_id)
self.assertEqual(tokens[-3], tokenizer.pad_token_id)
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
def test_tf_encode_plus_sent_to_model(self):
pass
@unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode.")
def test_torch_encode_plus_sent_to_model(self):
pass
def test_convert_tokens_to_string_format(self):
# The default common tokenizer tests assumes that the output of `convert_tokens_to_string` is a string which
# is not the case for Wav2vec2.
tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
tokens = ["T", "H", "I", "S", "|", "I", "S", "|", "A", "|", "T", "E", "X", "T"]
output = tokenizer.convert_tokens_to_string(tokens)
self.assertIsInstance(output["text"], str)
def test_nested_vocab(self):
eng_vocab = {"a": 7, "b": 8}
spa_vocab = {"a": 23, "c": 88}
ita_vocab = {"a": 6, "d": 9}
nested_vocab = {"eng": eng_vocab, "spa": spa_vocab, "ita": ita_vocab}
def check_tokenizer(tokenizer, check_ita_first=False):
if check_ita_first:
self.assertEqual(tokenizer.decode([6, 9, 9]), "ad")
self.assertEqual(tokenizer.encoder, ita_vocab)
tokenizer.set_target_lang("eng")
self.assertEqual(tokenizer.encoder, eng_vocab)
self.assertEqual(tokenizer.decode([7, 8, 7]), "aba")
tokenizer.set_target_lang("spa")
self.assertEqual(tokenizer.decode([23, 88, 23]), "aca")
self.assertEqual(tokenizer.encoder, spa_vocab)
tokenizer.set_target_lang("eng")
self.assertEqual(tokenizer.encoder, eng_vocab)
self.assertEqual(tokenizer.decode([7, 7, 8]), "ab")
tokenizer.set_target_lang("ita")
self.assertEqual(tokenizer.decode([6, 9, 9]), "ad")
self.assertEqual(tokenizer.encoder, ita_vocab)
with tempfile.TemporaryDirectory() as tempdir:
tempfile_path = os.path.join(tempdir, "vocab.json")
with open(tempfile_path, "w") as temp_file:
json.dump(nested_vocab, temp_file)
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(tempdir, target_lang="eng")
check_tokenizer(tokenizer)
with tempfile.TemporaryDirectory() as tempdir:
# should have saved target lang as "ita" since it was last one
tokenizer.save_pretrained(tempdir)
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(tempdir)
self.assertEqual(tokenizer.target_lang, "ita")
check_tokenizer(tokenizer, check_ita_first=True)
| 40,308 | 48.157317 | 1,215 | py |
transformers | transformers-main/tests/models/wav2vec2/test_modeling_wav2vec2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Wav2Vec2 model. """
import gc
import math
import multiprocessing
import os
import pickle
import tempfile
import traceback
import unittest
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2Config, is_torch_available
from transformers.testing_utils import (
CaptureLogger,
is_pt_flax_cross_test,
is_pyctcdecode_available,
is_torchaudio_available,
require_pyctcdecode,
require_soundfile,
require_torch,
require_torchaudio,
run_test_in_subprocess,
slow,
torch_device,
)
from transformers.utils import is_torch_fx_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import (
ModelTesterMixin,
_config_zero_init,
floats_tensor,
ids_tensor,
random_attention_mask,
)
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from safetensors.torch import save_file as safe_save_file
from transformers import (
Wav2Vec2FeatureExtractor,
Wav2Vec2ForAudioFrameClassification,
Wav2Vec2ForCTC,
Wav2Vec2ForMaskedLM,
Wav2Vec2ForPreTraining,
Wav2Vec2ForSequenceClassification,
Wav2Vec2ForXVector,
Wav2Vec2Model,
Wav2Vec2Processor,
)
from transformers.models.wav2vec2.modeling_wav2vec2 import (
WAV2VEC2_ADAPTER_PT_FILE,
WAV2VEC2_ADAPTER_SAFE_FILE,
Wav2Vec2GumbelVectorQuantizer,
_compute_mask_indices,
_sample_negative_indices,
)
if is_torchaudio_available():
import torchaudio
if is_pyctcdecode_available():
import pyctcdecode.decoder
from transformers import Wav2Vec2ProcessorWithLM
from transformers.models.wav2vec2_with_lm import processing_wav2vec2_with_lm
if is_torch_fx_available():
from transformers.utils.fx import symbolic_trace
def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = torchaudio.functional.resample(
torch.tensor(sample["audio"]["array"]), 48_000, 16_000
).numpy()
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm").to(
torch_device
)
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values.to(torch_device)).logits
# use a spawn pool, which should trigger a warning if different than fork
with CaptureLogger(pyctcdecode.decoder.logger) as cl, multiprocessing.get_context("spawn").Pool(1) as pool:
transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
# force batch_decode to internally create a spawn pool, which should trigger a warning if different than fork
multiprocessing.set_start_method("spawn", force=True)
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl:
transcription = processor.batch_decode(logits.cpu().numpy()).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class Wav2Vec2ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=16,
feat_extract_norm="group",
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=4,
num_attention_heads=2,
hidden_dropout_prob=0.1, # this is most likely not correctly set yet
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
mask_time_prob=0.5,
mask_time_length=2,
vocab_size=32,
do_stable_layer_norm=False,
num_adapter_layers=1,
adapter_stride=2,
tdnn_dim=(32, 32),
tdnn_kernel=(5, 3),
tdnn_dilation=(1, 2),
xvector_output_dim=32,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.num_adapter_layers = num_adapter_layers
self.adapter_stride = adapter_stride
self.mask_time_prob = mask_time_prob
self.mask_time_length = mask_time_length
self.scope = scope
self.tdnn_dim = tdnn_dim
self.tdnn_kernel = tdnn_kernel
self.tdnn_dilation = tdnn_dilation
self.xvector_output_dim = xvector_output_dim
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
self.adapter_output_seq_length = (self.output_seq_length - 1) // adapter_stride + 1
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = self.get_config()
return config, input_values, attention_mask
def get_config(self):
return Wav2Vec2Config(
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
mask_time_prob=self.mask_time_prob,
mask_time_length=self.mask_time_length,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
do_stable_layer_norm=self.do_stable_layer_norm,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
num_adapter_layers=self.num_adapter_layers,
adapter_stride=self.adapter_stride,
tdnn_dim=self.tdnn_dim,
tdnn_kernel=self.tdnn_kernel,
tdnn_dilation=self.tdnn_dilation,
xvector_output_dim=self.xvector_output_dim,
)
def create_and_check_model(self, config, input_values, attention_mask):
model = Wav2Vec2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter(self, config, input_values, attention_mask):
config.add_adapter = True
model = Wav2Vec2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.adapter_output_seq_length, self.hidden_size)
)
def create_and_check_model_with_adapter_for_ctc(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 2 * config.hidden_size
model = Wav2Vec2ForCTC(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.adapter_output_seq_length, self.vocab_size)
)
def create_and_check_model_with_adapter_proj_dim(self, config, input_values, attention_mask):
config.add_adapter = True
config.output_hidden_size = 8
model = Wav2Vec2Model(config=config)
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.adapter_output_seq_length, config.output_hidden_size),
)
def create_and_check_model_with_attn_adapter(self, config, input_values, attention_mask):
config.adapter_attn_dim = 16
model = Wav2Vec2ForCTC(config=config)
self.parent.assertIsNotNone(model._get_adapters())
model.to(torch_device)
model.eval()
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.output_seq_length, self.vocab_size))
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
model = Wav2Vec2Model(config=config)
model.to(torch_device)
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0.0
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = Wav2Vec2ForCTC(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
self.parent.assertTrue(isinstance(sum_loss, float))
self.parent.assertTrue(isinstance(mean_loss, float))
def check_seq_classifier_loss(self, config, input_values, *args):
model = Wav2Vec2ForSequenceClassification(config=config)
model.to(torch_device)
# make sure that dropout is disabled
model.eval()
input_values = input_values[:3]
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
unmasked_loss = model(input_values, labels=labels).loss.item()
self.parent.assertTrue(isinstance(masked_loss, float))
self.parent.assertTrue(isinstance(unmasked_loss, float))
self.parent.assertTrue(masked_loss != unmasked_loss)
def check_ctc_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ForCTC(config=config)
model.to(torch_device)
model.train()
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
if max_length_labels[i] < labels.shape[-1]:
# it's important that we make sure that target lenghts are at least
# one shorter than logit lenghts to prevent -inf
labels[i, max_length_labels[i] - 1 :] = -100
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_seq_classifier_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ForSequenceClassification(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_xvector_training(self, config, input_values, *args):
config.ctc_zero_infinity = True
model = Wav2Vec2ForXVector(config=config)
model.to(torch_device)
model.train()
# freeze everything but the classification head
model.freeze_base_model()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
loss = model(input_values, labels=labels).loss
self.parent.assertFalse(torch.isinf(loss).item())
loss.backward()
def check_labels_out_of_vocab(self, config, input_values, *args):
model = Wav2Vec2ForCTC(config)
model.to(torch_device)
model.train()
input_values = input_values[:3]
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
with self.parent.assertRaises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class Wav2Vec2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(Wav2Vec2ForCTC, Wav2Vec2Model, Wav2Vec2ForMaskedLM, Wav2Vec2ForSequenceClassification, Wav2Vec2ForPreTraining)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"audio-classification": Wav2Vec2ForSequenceClassification,
"automatic-speech-recognition": Wav2Vec2ForCTC,
"feature-extraction": Wav2Vec2Model,
"fill-mask": Wav2Vec2ForMaskedLM,
}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = False
test_headmasking = False
def setUp(self):
self.model_tester = Wav2Vec2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_for_ctc(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_for_ctc(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Wav2Vec2 has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Wav2Vec2 cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Wav2Vec2 has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_flax_to_pt(self):
pass
@is_pt_flax_cross_test
# non-robust architecture does not exist in Flax
def test_equivalence_pt_to_flax(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
def test_mask_feature_prob_ctc(self):
model = Wav2Vec2ForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", mask_feature_prob=0.2, mask_feature_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_prob_ctc(self):
model = Wav2Vec2ForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", mask_time_prob=0.2, mask_time_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
@slow
def test_model_from_pretrained(self):
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
# Wav2Vec2 cannot be torchscripted because of group norm.
def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
if not is_torch_fx_available() or not self.fx_compatible:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.return_dict = False
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.to(torch_device)
model.eval()
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=output_loss)
try:
input_names = [
"attention_mask",
"bbox",
"input_features",
"input_ids",
"input_values",
"pixel_values",
"token_type_ids",
"visual_feats",
"visual_pos",
]
labels = inputs.get("labels", None)
start_positions = inputs.get("start_positions", None)
end_positions = inputs.get("end_positions", None)
if labels is not None:
input_names.append("labels")
if start_positions is not None:
input_names.append("start_positions")
if end_positions is not None:
input_names.append("end_positions")
filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
input_names = list(filtered_inputs.keys())
model_output = model(**filtered_inputs)
if (
isinstance(model, Wav2Vec2ForSequenceClassification)
and not hasattr(model.config, "problem_type")
or model.config.problem_type is None
):
model.config.problem_type = "single_label_classification"
traced_model = symbolic_trace(model, input_names)
traced_output = traced_model(**filtered_inputs)
except Exception as e:
self.fail(f"Couldn't trace module: {e}")
def flatten_output(output):
flatten = []
for x in output:
if isinstance(x, (tuple, list)):
flatten += flatten_output(x)
elif not isinstance(x, torch.Tensor):
continue
else:
flatten.append(x)
return flatten
model_output = flatten_output(model_output)
traced_output = flatten_output(traced_output)
num_outputs = len(model_output)
for i in range(num_outputs):
self.assertTrue(
torch.allclose(model_output[i], traced_output[i]),
f"traced {i}th output doesn't match model {i}th output for {model_class}",
)
# Test that the model can be serialized and restored properly
with tempfile.TemporaryDirectory() as tmp_dir_name:
pkl_file_name = os.path.join(tmp_dir_name, "model.pkl")
try:
with open(pkl_file_name, "wb") as f:
pickle.dump(traced_model, f)
with open(pkl_file_name, "rb") as f:
loaded = pickle.load(f)
except Exception as e:
self.fail(f"Couldn't serialize / deserialize the traced model: {e}")
loaded_output = loaded(**filtered_inputs)
loaded_output = flatten_output(loaded_output)
for i in range(num_outputs):
self.assertTrue(
torch.allclose(model_output[i], loaded_output[i]),
f"serialized model {i}th output doesn't match model {i}th output for {model_class}",
)
# Avoid memory leak. Without this, each call increase RAM usage by ~20MB.
# (Even with this call, there are still memory leak by ~0.04MB)
self.clear_torch_jit_class_registry()
@require_torch
class Wav2Vec2RobustModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
Wav2Vec2ForCTC,
Wav2Vec2Model,
Wav2Vec2ForMaskedLM,
Wav2Vec2ForSequenceClassification,
Wav2Vec2ForPreTraining,
Wav2Vec2ForAudioFrameClassification,
Wav2Vec2ForXVector,
)
if is_torch_available()
else ()
)
test_pruning = False
test_headmasking = False
def setUp(self):
self.model_tester = Wav2Vec2ModelTester(
self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True
)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_with_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter(*config_and_inputs)
def test_model_with_adapter_proj_dim(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_adapter_proj_dim(*config_and_inputs)
def test_model_with_attn_adapter(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_with_attn_adapter(*config_and_inputs)
def test_batched_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_batch_inference(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
def test_seq_classifier_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
def test_ctc_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_training(*config_and_inputs)
def test_seq_classifier_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_seq_classifier_training(*config_and_inputs)
def test_xvector_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_xvector_training(*config_and_inputs)
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
# Wav2Vec2 has no inputs_embeds
def test_inputs_embeds(self):
pass
# `input_ids` is renamed to `input_values`
def test_forward_signature(self):
pass
# Wav2Vec2 cannot resize token embeddings
# since it has no tokens embeddings
def test_resize_tokens_embeddings(self):
pass
# Wav2Vec2 has no inputs_embeds
# and thus the `get_input_embeddings` fn
# is not implemented
def test_model_common_attributes(self):
pass
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
# set layer drop to 0
model.config.layerdrop = 0.0
input_values = inputs_dict["input_values"]
input_lengths = torch.tensor(
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
)
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
inputs_dict["labels"] = labels
outputs = model(**inputs_dict)
output = outputs[0]
# Encoder-/Decoder-only models
hidden_states = outputs.hidden_states[0]
attentions = outputs.attentions[0]
hidden_states.retain_grad()
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
self.assertIsNotNone(attentions.grad)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
uniform_init_parms = [
"conv.weight",
"masked_spec_embed",
"codevectors",
"quantizer.weight_proj.weight",
"project_hid.weight",
"project_hid.bias",
"project_q.weight",
"project_q.bias",
"feature_projection.projection.weight",
"feature_projection.projection.bias",
"objective.weight",
]
if param.requires_grad:
if any(x in name for x in uniform_init_parms):
self.assertTrue(
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
else:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
# overwrite from test_modeling_common
def _mock_init_weights(self, module):
if hasattr(module, "weight") and module.weight is not None:
module.weight.data.fill_(3)
if hasattr(module, "weight_g") and module.weight_g is not None:
module.weight_g.data.fill_(3)
if hasattr(module, "weight_v") and module.weight_v is not None:
module.weight_v.data.fill_(3)
if hasattr(module, "bias") and module.bias is not None:
module.bias.data.fill_(3)
if hasattr(module, "codevectors") and module.codevectors is not None:
module.codevectors.data.fill_(3)
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
module.masked_spec_embed.data.fill_(3)
def test_model_for_pretraining(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
model = Wav2Vec2ForPreTraining(config).to(torch_device)
batch_size = inputs_dict["input_values"].shape[0]
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
features_shape = (batch_size, feature_seq_length)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
sampled_negative_indices = _sample_negative_indices(features_shape, 10, mask_time_indices)
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
loss = model(
inputs_dict["input_values"],
attention_mask=inputs_dict["attention_mask"],
mask_time_indices=mask_time_indices,
sampled_negative_indices=sampled_negative_indices,
).loss
# more losses
mask_time_indices[:, : mask_time_indices.shape[-1] // 2] = True
sampled_negative_indices = _sample_negative_indices(features_shape, 10, mask_time_indices.cpu().numpy())
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
loss_more_masked = model(
inputs_dict["input_values"],
attention_mask=inputs_dict["attention_mask"],
mask_time_indices=mask_time_indices,
sampled_negative_indices=sampled_negative_indices,
).loss
# loss_more_masked has to be bigger or equal loss since more masked inputs have to be predicted
self.assertTrue(loss.detach().item() <= loss_more_masked.detach().item())
def test_mask_feature_prob_ctc(self):
model = Wav2Vec2ForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", mask_feature_prob=0.2, mask_feature_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_prob_ctc(self):
model = Wav2Vec2ForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", mask_time_prob=0.2, mask_time_length=2
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [1, 3, 2, 6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (4, 1498, 32))
def test_mask_time_feature_prob_ctc_single_batch(self):
model = Wav2Vec2ForCTC.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2",
mask_time_prob=0.2,
mask_feature_prob=0.2,
mask_time_length=2,
mask_feature_length=2,
)
model.to(torch_device).train()
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
batch_duration_in_seconds = [6]
input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds]
batch = processor(
input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
)
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
self.assertEqual(logits.shape, (1, 1498, 32))
@unittest.skip(reason="Feed forward chunking is not implemented")
def test_feed_forward_chunking(self):
pass
def test_load_and_set_attn_adapter(self):
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
def get_logits(model, input_features):
model = model.to(torch_device)
batch = processor(
input_features,
padding=True,
sampling_rate=processor.feature_extractor.sampling_rate,
return_tensors="pt",
)
with torch.no_grad():
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
return logits
input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter", target_lang="it")
logits = get_logits(model, input_features)
model_2 = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
model_2.load_adapter("it")
logits_2 = get_logits(model_2, input_features)
self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
def test_load_attn_adapter(self):
processor = Wav2Vec2Processor.from_pretrained(
"hf-internal-testing/tiny-random-wav2vec2", return_attention_mask=True
)
def get_logits(model, input_features):
model = model.to(torch_device)
batch = processor(
input_features,
padding=True,
sampling_rate=processor.feature_extractor.sampling_rate,
return_tensors="pt",
)
with torch.no_grad():
logits = model(
input_values=batch["input_values"].to(torch_device),
attention_mask=batch["attention_mask"].to(torch_device),
).logits
return logits
input_features = [np.random.random(16_000 * s) for s in [1, 3, 2, 6]]
model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2", adapter_attn_dim=16)
with tempfile.TemporaryDirectory() as tempdir:
model.save_pretrained(tempdir)
model = Wav2Vec2ForCTC.from_pretrained(tempdir)
logits = get_logits(model, input_features)
adapter_weights = model._get_adapters()
# save safe weights
safe_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_SAFE_FILE.format("eng"))
safe_save_file(adapter_weights, safe_filepath, metadata={"format": "pt"})
model.load_adapter("eng")
model.load_adapter("eng", use_safetensors=True)
with self.assertRaises(OSError):
model.load_adapter("eng", use_safetensors=False)
with self.assertRaises(Exception):
model.load_adapter("ita", use_safetensors=True)
logits_2 = get_logits(model, input_features)
self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
with tempfile.TemporaryDirectory() as tempdir:
model.save_pretrained(tempdir)
model = Wav2Vec2ForCTC.from_pretrained(tempdir)
logits = get_logits(model, input_features)
adapter_weights = model._get_adapters()
# save pt weights
pt_filepath = os.path.join(tempdir, WAV2VEC2_ADAPTER_PT_FILE.format("eng"))
torch.save(adapter_weights, pt_filepath)
model.load_adapter("eng")
model.load_adapter("eng", use_safetensors=False)
with self.assertRaises(OSError):
model.load_adapter("eng", use_safetensors=True)
logits_2 = get_logits(model, input_features)
self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
model = Wav2Vec2ForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-adapter")
logits = get_logits(model, input_features)
model.load_adapter("eng")
model.load_adapter("eng", use_safetensors=False)
model.load_adapter("eng", use_safetensors=True)
logits_2 = get_logits(model, input_features)
self.assertTrue(torch.allclose(logits, logits_2, atol=1e-3))
@slow
def test_model_from_pretrained(self):
model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
@require_torch
class Wav2Vec2UtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_low_prob(self):
# with these settings num_masked_spans=0.5, which means probabilistic rounding
# ensures that in 5 out of 10 method calls, num_masked_spans=0, and in
# the other 5 out of 10, cases num_masked_spans=1
n_trials = 100
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
count_dimensions_masked = 0
count_dimensions_not_masked = 0
for _ in range(n_trials):
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
num_masks = torch.sum(mask).item()
if num_masks > 0:
count_dimensions_masked += 1
else:
count_dimensions_not_masked += 1
# as we test for at least 10 masked dimension and at least
# 10 non-masked dimension, this test could fail with probability:
# P(100 coin flips, at most 9 heads) = 1.66e-18
self.assertGreater(count_dimensions_masked, int(n_trials * 0.1))
self.assertGreater(count_dimensions_not_masked, int(n_trials * 0.1))
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
mask = torch.from_numpy(mask).to(torch_device)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
mask = torch.from_numpy(mask).to(torch_device)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_mask_indices_short_audio(self):
batch_size = 4
sequence_length = 100
mask_prob = 0.05
mask_length = 10
attention_mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
# force one example to be heavily padded
attention_mask[0, 5:] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask, min_masks=2
)
# make sure that non-padded examples cannot be padded
self.assertFalse(mask[0][attention_mask[0].to(torch.bool).cpu()].any())
def test_compute_perplexity(self):
probs = torch.arange(100, device=torch_device).reshape(2, 5, 10) / 100
ppl = Wav2Vec2GumbelVectorQuantizer._compute_perplexity(probs)
self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3)
# mask half of the input
mask = torch.ones((2,), device=torch_device, dtype=torch.bool)
mask[0] = 0
ppl = Wav2Vec2GumbelVectorQuantizer._compute_perplexity(probs, mask)
self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3)
def test_sample_negatives(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
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()
# sample negative indices
sampled_negative_indices = _sample_negative_indices((batch_size, sequence_length), num_negatives, None)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertEqual(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
def test_sample_negatives_with_mask(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
# second half of last input tensor is padded
mask = torch.ones((batch_size, sequence_length), dtype=torch.long, device=torch_device)
mask[-1, sequence_length // 2 :] = 0
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()
# replace masked feature vectors with -100 to test that those are not sampled
features = torch.where(mask[:, :, None].expand(features.shape).bool(), features, -100)
# sample negative indices
sampled_negative_indices = _sample_negative_indices(
(batch_size, sequence_length), num_negatives, mask.cpu().numpy()
)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
negatives = features.view(-1, hidden_size)[sampled_negative_indices.long().view(-1)]
negatives = negatives.view(batch_size, sequence_length, -1, hidden_size).permute(2, 0, 1, 3)
self.assertTrue((negatives >= 0).all().item())
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors => this means that `unique()` yields a single value for `hidden_size` dim
self.assertEqual(negatives.unique(dim=-1).shape, (num_negatives, batch_size, sequence_length, 1))
@require_torch
@require_soundfile
@slow
class Wav2Vec2ModelIntegrationTest(unittest.TestCase):
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
ds = load_dataset("anton-l/superb_dummy", task, split="test")
return ds[:num_samples]
def test_inference_ctc_normal(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
input_speech = self._load_datasamples(1)
input_values = processor(input_speech, return_tensors="pt").input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_normal_batched(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
model.to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
input_speech = self._load_datasamples(2)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_robust_batched(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
" him with the thousands of spectators were trivialities not worth thinking about",
"his instant panic was followed by a small sharp blow high on his chest",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
@unittest.skipIf(torch_device != "cpu", "cannot make deterministic on GPU")
def test_inference_integration(self):
model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
model.to(torch_device)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base")
input_speech = self._load_datasamples(2)
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
batch_size = inputs_dict["input_values"].shape[0]
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
features_shape = (batch_size, feature_seq_length)
np.random.seed(4)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
with torch.no_grad():
outputs = model(
inputs_dict.input_values.to(torch_device),
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)
# retrieve cosine sim of masked features
cosine_sim_masked = cosine_sim[mask_time_indices]
# cosine similarity of model is all > 0.5 as model is
# pre-trained on contrastive loss
# fmt: off
expected_cosine_sim_masked = torch.tensor([
0.8523, 0.5860, 0.6905, 0.5557, 0.7456, 0.5249, 0.6639, 0.7654, 0.7565,
0.8167, 0.8222, 0.7960, 0.8034, 0.8166, 0.8310, 0.8263, 0.8274, 0.8258,
0.8179, 0.8412, 0.8536, 0.5098, 0.4728, 0.6461, 0.4498, 0.6002, 0.5774,
0.6457, 0.7123, 0.5668, 0.6866, 0.4960, 0.6293, 0.7423, 0.7419, 0.7526,
0.7768, 0.4898, 0.5393, 0.8183
], device=torch_device)
# fmt: on
self.assertTrue(torch.allclose(cosine_sim_masked, expected_cosine_sim_masked, atol=1e-3))
def test_inference_pretrained(self):
model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
model.to(torch_device)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"facebook/wav2vec2-base", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
batch_size = inputs_dict["input_values"].shape[0]
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
features_shape = (batch_size, feature_seq_length)
torch.manual_seed(0)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
with torch.no_grad():
outputs = model(
inputs_dict.input_values.to(torch_device),
attention_mask=inputs_dict.attention_mask.to(torch_device),
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1)
# retrieve cosine sim of masked features
cosine_sim_masked = cosine_sim[mask_time_indices]
# ... now compare to randomly initialized model
config = Wav2Vec2Config.from_pretrained("facebook/wav2vec2-base")
model_rand = Wav2Vec2ForPreTraining(config).to(torch_device).eval()
with torch.no_grad():
outputs_rand = model_rand(
inputs_dict.input_values.to(torch_device),
attention_mask=inputs_dict.attention_mask.to(torch_device),
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim_rand = torch.cosine_similarity(
outputs_rand.projected_states, outputs_rand.projected_quantized_states, dim=-1
)
# retrieve cosine sim of masked features
cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices]
# a pretrained wav2vec2 model has learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states > 0.5
# a random wav2vec2 model has not learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states is very likely < 0.1
self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)
@unittest.skipIf(torch_device != "cpu", "cannot make deterministic on GPU")
def test_loss_pretraining(self):
model = Wav2Vec2ForPreTraining.from_pretrained(
"facebook/wav2vec2-base",
attention_dropout=0.0,
feat_proj_dropout=0.0,
hidden_dropout=0.0,
layerdrop=0.0,
)
model.to(torch_device).train()
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"facebook/wav2vec2-base", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True)
batch_size = inputs_dict["input_values"].shape[0]
feature_seq_length = int(model._get_feat_extract_output_lengths(inputs_dict["input_values"].shape[1]))
features_shape = (batch_size, feature_seq_length)
torch.manual_seed(0)
np.random.seed(0)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
sampled_negative_indices = _sample_negative_indices(
mask_time_indices.shape, model.config.num_negatives, mask_time_indices
)
mask_time_indices = torch.from_numpy(mask_time_indices).to(torch_device)
sampled_negative_indices = torch.from_numpy(sampled_negative_indices).to(torch_device)
with torch.no_grad():
outputs = model(
inputs_dict.input_values.to(torch_device),
attention_mask=inputs_dict.attention_mask.to(torch_device),
mask_time_indices=mask_time_indices,
sampled_negative_indices=sampled_negative_indices,
)
# check diversity loss
num_codevectors = model.config.num_codevectors_per_group * model.config.num_codevector_groups
diversity_loss = (num_codevectors - outputs.codevector_perplexity) / num_codevectors
self.assertTrue(abs(diversity_loss.item() - 0.9538) < 1e-3)
# check overall loss (contrastive loss + diversity loss)
expected_loss = 116.7094
self.assertTrue(abs(outputs.loss.item() - expected_loss) < 1e-3)
def test_inference_keyword_spotting(self):
model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
input_data = self._load_superb("ks", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
expected_labels = [7, 6, 10, 9]
# s3prl logits for the same batch
expected_logits = torch.tensor([6.1186, 11.8961, 10.2931, 6.0898], device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
def test_inference_intent_classification(self):
model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ic")
input_data = self._load_superb("ic", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits_action, predicted_ids_action = torch.max(outputs.logits[:, :6], dim=-1)
predicted_logits_object, predicted_ids_object = torch.max(outputs.logits[:, 6:20], dim=-1)
predicted_logits_location, predicted_ids_location = torch.max(outputs.logits[:, 20:24], dim=-1)
expected_labels_action = [0, 0, 2, 3]
expected_logits_action = torch.tensor([0.4568, 11.0848, 1.6621, 9.3841], device=torch_device)
expected_labels_object = [3, 10, 3, 4]
expected_logits_object = torch.tensor([1.5322, 10.7094, 5.2469, 22.1318], device=torch_device)
expected_labels_location = [0, 0, 0, 1]
expected_logits_location = torch.tensor([1.5335, 6.5096, 10.5704, 11.0569], device=torch_device)
self.assertListEqual(predicted_ids_action.tolist(), expected_labels_action)
self.assertListEqual(predicted_ids_object.tolist(), expected_labels_object)
self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=1e-2))
self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=1e-2))
self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=1e-2))
def test_inference_speaker_identification(self):
model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid")
input_data = self._load_superb("si", 4)
output_logits = []
with torch.no_grad():
for example in input_data["speech"]:
input = processor(example, return_tensors="pt", padding=True)
output = model(input.input_values.to(torch_device), attention_mask=None)
output_logits.append(output.logits[0])
output_logits = torch.stack(output_logits)
predicted_logits, predicted_ids = torch.max(output_logits, dim=-1)
expected_labels = [251, 1, 1, 3]
# s3prl logits for the same batch
expected_logits = torch.tensor([37.5627, 71.6362, 64.2419, 31.7778], device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
def test_inference_emotion_recognition(self):
model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er")
input_data = self._load_superb("er", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = torch.max(outputs.logits, dim=-1)
expected_labels = [1, 1, 2, 2]
# s3prl logits for the same batch
expected_logits = torch.tensor([2.1722, 3.0779, 8.0287, 6.6797], device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-2))
def test_phoneme_recognition(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft").to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="pt", padding=True)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
# should correspond to =>:
# [
# "a man said to the universe sir i exist",
# "sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
# "the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around him with the thousands of spectators were trivialities not worth thinking about",
# "his instant panic was followed by a small sharp blow high on his chest",
# ]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
@require_pyctcdecode
@require_torchaudio
def test_wav2vec2_with_lm(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = torchaudio.functional.resample(
torch.tensor(sample["audio"]["array"]), 48_000, 16_000
).numpy()
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm").to(
torch_device
)
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values.to(torch_device)).logits
transcription = processor.batch_decode(logits.cpu().numpy()).text
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
@require_pyctcdecode
@require_torchaudio
def test_wav2vec2_with_lm_pool(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = torchaudio.functional.resample(
torch.tensor(sample["audio"]["array"]), 48_000, 16_000
).numpy()
model = Wav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm").to(
torch_device
)
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values.to(torch_device)).logits
# test user-managed pool
with multiprocessing.get_context("fork").Pool(2) as pool:
transcription = processor.batch_decode(logits.cpu().numpy(), pool).text
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
# user-managed pool + num_processes should trigger a warning
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl, multiprocessing.get_context("fork").Pool(
2
) as pool:
transcription = processor.batch_decode(logits.cpu().numpy(), pool, num_processes=2).text
self.assertIn("num_process", cl.out)
self.assertIn("it will be ignored", cl.out)
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
@require_pyctcdecode
@require_torchaudio
def test_wav2vec2_with_lm_invalid_pool(self):
run_test_in_subprocess(test_case=self, target_func=_test_wav2vec2_with_lm_invalid_pool, inputs=None)
def test_inference_diarization(self):
model = Wav2Vec2ForAudioFrameClassification.from_pretrained("anton-l/wav2vec2-base-superb-sd").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sd")
input_data = self._load_superb("sd", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000)
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask)
# labels is a one-hot array of shape (num_frames, num_speakers)
labels = (outputs.logits > 0).long()
# s3prl logits for the same batch
expected_logits = torch.tensor(
[
[[-5.2807, -5.1272], [-5.4059, -4.7757], [-5.2764, -4.9621], [-5.0117, -4.5851]],
[[-1.7643, -0.5462], [-1.7369, -0.2649], [-1.5066, -0.6200], [-4.5703, -2.4863]],
[[-0.8656, -0.4783], [-0.8899, -0.3289], [-0.9267, -0.5781], [-0.7817, -0.4619]],
[[-4.8625, -2.5316], [-5.2339, -2.2155], [-4.9835, -2.0344], [-4.4727, -1.8421]],
],
device=torch_device,
)
self.assertEqual(labels[0, :, 0].sum(), 555)
self.assertEqual(labels[0, :, 1].sum(), 299)
# TODO: update the tolerance after the CI moves to torch 1.10
self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2))
def test_inference_speaker_verification(self):
model = Wav2Vec2ForXVector.from_pretrained("anton-l/wav2vec2-base-superb-sv").to(torch_device)
processor = Wav2Vec2FeatureExtractor.from_pretrained("anton-l/wav2vec2-base-superb-sv")
input_data = self._load_superb("si", 4)
inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000)
labels = torch.tensor([5, 1, 1, 3], device=torch_device).T
with torch.no_grad():
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
outputs = model(input_values, attention_mask=attention_mask, labels=labels)
embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1).cpu()
cosine_sim = torch.nn.CosineSimilarity(dim=-1)
# id10002 vs id10002
self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).numpy(), 0.9758, 3)
# id10006 vs id10002
self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).numpy(), 0.7579, 3)
# id10002 vs id10004
self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).numpy(), 0.7594, 3)
# TODO: update the tolerance after the CI moves to torch 1.10
self.assertAlmostEqual(outputs.loss.item(), 17.7963, 2)
@require_torchaudio
def test_inference_mms_1b_all(self):
model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all").to(torch_device)
processor = Wav2Vec2Processor.from_pretrained("facebook/mms-1b-all")
LANG_MAP = {"it": "ita", "es": "spa", "fr": "fra", "en": "eng"}
def run_model(lang):
ds = load_dataset("common_voice", lang, split="test", streaming=True)
sample = next(iter(ds))
wav2vec2_lang = LANG_MAP[lang]
model.load_adapter(wav2vec2_lang)
processor.tokenizer.set_target_lang(wav2vec2_lang)
resampled_audio = torchaudio.functional.resample(
torch.tensor(sample["audio"]["array"]), 48_000, 16_000
).numpy()
inputs = processor(resampled_audio, sampling_rate=16_000, return_tensors="pt")
input_values = inputs.input_values.to(torch_device)
attention_mask = inputs.attention_mask.to(torch_device)
with torch.no_grad():
outputs = model(input_values, attention_mask=attention_mask).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
return transcription
TRANSCRIPTIONS = {
"it": "mi hanno fatto un'offerta che non potevo proprio rifiutare",
"es": "bien y qué regalo vas a abrir primero",
"fr": "un vrai travail intéressant va enfin être mené sur ce sujet",
"en": "twas the time of day and olof spen slept during the summer",
}
for lang in LANG_MAP.keys():
assert run_model(lang) == TRANSCRIPTIONS[lang]
| 81,833 | 40.944644 | 198 | py |
transformers | transformers-main/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import copy
import gc
import glob
import inspect
import math
import multiprocessing
import os
import tempfile
import traceback
import unittest
import numpy as np
import pytest
from datasets import load_dataset
from huggingface_hub import snapshot_download
from transformers import Wav2Vec2Config, is_tf_available
from transformers.testing_utils import (
CaptureLogger,
is_flaky,
is_pt_tf_cross_test,
require_librosa,
require_pyctcdecode,
require_tf,
run_test_in_subprocess,
slow,
)
from transformers.utils import is_librosa_available, is_pyctcdecode_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoFeatureExtractor,
TFWav2Vec2ForCTC,
TFWav2Vec2ForSequenceClassification,
TFWav2Vec2Model,
Wav2Vec2Processor,
)
from transformers.models.wav2vec2.modeling_tf_wav2vec2 import _compute_mask_indices
if is_pyctcdecode_available():
import pyctcdecode.decoder
from transformers import Wav2Vec2ProcessorWithLM
from transformers.models.wav2vec2_with_lm import processing_wav2vec2_with_lm
if is_librosa_available():
import librosa
def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
downloaded_folder = snapshot_download("patrickvonplaten/common_voice_es_sample")
file_path = glob.glob(downloaded_folder + "/*")[0]
sample = librosa.load(file_path, sr=16_000)[0]
model = TFWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(sample, return_tensors="tf").input_values
logits = model(input_values).logits
# use a spawn pool, which should trigger a warning if different than fork
with CaptureLogger(pyctcdecode.decoder.logger) as cl, multiprocessing.get_context("spawn").Pool(1) as pool:
transcription = processor.batch_decode(logits.numpy(), pool).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
# force batch_decode to internally create a spawn pool, which should trigger a warning if different than fork
multiprocessing.set_start_method("spawn", force=True)
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl:
transcription = processor.batch_decode(logits.numpy()).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
@require_tf
class TFWav2Vec2ModelTester:
def __init__(
self,
parent,
batch_size=3,
seq_length=1024,
is_training=False,
hidden_size=16,
feat_extract_norm="group",
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=2,
num_attention_heads=2,
hidden_dropout_prob=0.1, # this is most likely not correctly set yet
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
vocab_size=32,
do_stable_layer_norm=False,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.scope = scope
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = tf.cast(ids_tensor([self.batch_size, self.seq_length], 32768), tf.float32) / 32768.0
attention_mask = tf.ones_like(input_values)
config = Wav2Vec2Config(
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
do_stable_layer_norm=self.do_stable_layer_norm,
)
return config, input_values, attention_mask
def create_and_check_model(self, config, input_values, attention_mask):
model = TFWav2Vec2Model(config)
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
)
def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
config.layerdrop = 0.0
model = TFWav2Vec2Model(config)
input_values = input_values[:3]
attention_mask = tf.ones_like(input_values)
input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
# convert values that are over input_lengths to padding
input_values = input_values * length_mask
attention_mask = attention_mask * length_mask
batch_outputs = model(input_values, attention_mask=attention_mask, training=False).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i : i + 1, : input_lengths[i]]
output = model(input_slice, training=False).last_hidden_state
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
self.parent.assertTrue(np.allclose(output, batch_output, atol=1e-3))
def check_ctc_loss(self, config, input_values, *args):
model = TFWav2Vec2ForCTC(config)
input_values = input_values[:3]
attention_mask = tf.ones_like(input_values)
input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
# convert values that are over input_lengths to padding
input_values = input_values * length_mask
attention_mask = attention_mask * length_mask
model.config.ctc_loss_reduction = "sum"
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
model.config.ctc_loss_reduction = "mean"
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss
self.parent.assertTrue(abs(labels.shape[0] * mean_loss - sum_loss) < 1e-2)
def check_seq_classifier_loss(self, loss, config, input_values, *args):
model = TFWav2Vec2ForSequenceClassification(config)
input_values = input_values[:3]
attention_mask = tf.ones(input_values.shape, dtype=tf.int32)
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
labels = tf.random.uniform((input_values.shape[0],), maxval=len(model.config.id2label), dtype=tf.int32)
# pad input
for i in range(len(input_lengths)):
input_values[i, input_lengths[i] :] = 0.0
attention_mask[i, input_lengths[i] :] = 0
training = False
masked_loss = (
model(input_values, attention_mask=attention_mask, labels=labels, training=training).loss.numpy().item()
)
unmasked_loss = model(input_values, labels=labels, training=training).loss.numpy().item()
assert isinstance(masked_loss, float)
assert isinstance(unmasked_loss, float)
assert masked_loss != unmasked_loss
def check_training(self, config, input_values, *args):
model = TFWav2Vec2ForCTC(config)
# freeze feature encoder
model.freeze_feature_encoder()
input_values = input_values[:3]
input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
input_values = input_values * length_mask
pad_size = max(max_length_labels) - labels.shape[1]
labels = tf.pad(labels, ((0, 0), (0, pad_size)), constant_values=-100)
loss = model(input_values, labels=labels, training=True).loss
self.parent.assertFalse(tf.math.is_inf(loss))
def check_labels_out_of_vocab(self, config, input_values, *args):
model = TFWav2Vec2ForCTC(config)
input_lengths = tf.constant([input_values.shape[-1] // i for i in [4, 2, 1]])
max_length_labels = model.wav2vec2._get_feat_extract_output_lengths(input_lengths)
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size + 100)
with pytest.raises(ValueError):
model(input_values, labels=labels)
def prepare_config_and_inputs_for_common(self):
config, input_values, attention_mask = self.prepare_config_and_inputs()
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_tf
class TFWav2Vec2ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFWav2Vec2Model, TFWav2Vec2ForCTC, TFWav2Vec2ForSequenceClassification) if is_tf_available() else ()
)
pipeline_model_mapping = (
{"audio-classification": TFWav2Vec2ForSequenceClassification, "feature-extraction": TFWav2Vec2Model}
if is_tf_available()
else {}
)
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFWav2Vec2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
# overwrite because input_values != input_ids
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# overwrite because input_values != input_ids
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs_dict = model(inputs)
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_values = inputs_keywords.pop("input_values", None)
outputs_keywords = model(input_values, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_hidden_states_output(config, inputs_dict, model_class):
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
hidden_states = outputs.hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.output_seq_length, self.model_tester.hidden_size],
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(config, inputs_dict, model_class)
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(config, inputs_dict, model_class)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
@is_flaky()
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
def test_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_training(*config_and_inputs)
@unittest.skip(reason="Wav2Vec2 has no input embeddings")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Wav2Vec2 has no tokens embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Wav2Vec2 has no input embeddings")
def test_model_common_attributes(self):
pass
@slow
def test_model_from_pretrained(self):
model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
def test_dataset_conversion(self):
# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
pass
@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
def test_keras_fit(self):
# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
pass
@is_pt_tf_cross_test
def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
# We override the base test here to skip loss calculation for Wav2Vec2 models because the loss is massive with
# the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
import torch
import transformers
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
# of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
# TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
self._make_attention_mask_non_null(inputs_dict)
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
tf_model = model_class(config)
pt_model = pt_model_class(config)
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(
tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
)
pt_model = transformers.load_tf2_model_in_pytorch_model(
pt_model, tf_model, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
@require_tf
class TFWav2Vec2RobustModelTest(TFModelTesterMixin, unittest.TestCase):
all_model_classes = (
(TFWav2Vec2Model, TFWav2Vec2ForCTC, TFWav2Vec2ForSequenceClassification) if is_tf_available() else ()
)
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFWav2Vec2ModelTester(
self,
conv_stride=(3, 3, 3),
feat_extract_norm="layer",
do_stable_layer_norm=True,
scope="robust",
)
self.config_tester = ConfigTester(self, config_class=Wav2Vec2Config, hidden_size=37)
# overwrite because input_values != input_ids
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
# overwrite because input_values != input_ids
def test_keyword_and_dict_args(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs_dict = model(inputs)
inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
input_values = inputs_keywords.pop("input_values", None)
outputs_keywords = model(input_values, **inputs_keywords)
output_dict = outputs_dict[0].numpy()
output_keywords = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_hidden_states_output(config, inputs_dict, model_class):
model = model_class(config)
outputs = model(self._prepare_for_class(inputs_dict, model_class))
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
)
hidden_states = outputs.hidden_states
self.assertEqual(config.output_attentions, False)
self.assertEqual(len(hidden_states), expected_num_layers)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.output_seq_length, self.model_tester.hidden_size],
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(config, inputs_dict, model_class)
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(config, inputs_dict, model_class)
def test_batched_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_batch_inference(*config_and_inputs)
def test_ctc_loss_inference(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_ctc_loss(*config_and_inputs)
# TODO (Joao): fix me
@unittest.skip("Broke with TF 2.10")
def test_labels_out_of_vocab(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
def test_train(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_training(*config_and_inputs)
@unittest.skip(reason="Wav2Vec2 has no input embeddings")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Wav2Vec2 has no tokens embeddings")
def test_resize_tokens_embeddings(self):
pass
@unittest.skip(reason="Wav2Vec2 has no input embeddings")
def test_model_common_attributes(self):
pass
@slow
def test_model_from_pretrained(self):
model = TFWav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsNotNone(model)
@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
def test_dataset_conversion(self):
# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
pass
@unittest.skip(reason="Fix me! Wav2Vec2 hits OOM errors when loss is computed on full batch")
def test_keras_fit(self):
# TODO: (Amy) - check whether skipping CTC model resolves this issue and possible resolutions for CTC
pass
@is_pt_tf_cross_test
def test_pt_tf_model_equivalence(self, allow_missing_keys=False):
# We override the base test here to skip loss calculation for Wav2Vec2 models because the loss is massive with
# the default labels and frequently overflows to inf or exceeds numerical tolerances between TF/PT
import torch
import transformers
for model_class in self.all_model_classes:
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
# Output all for aggressive testing
config.output_hidden_states = True
config.output_attentions = self.has_attentions
# Make sure no sequence has all zeros as attention mask, otherwise some tests fail due to the inconsistency
# of the usage `1e-4`, `1e-9`, `1e-30`, `-inf`.
# TODO: Use a uniform value for all models, make sure all tests pass without this processing, and remove it.
self._make_attention_mask_non_null(inputs_dict)
pt_model_class_name = model_class.__name__[2:] # Skip the "TF" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
tf_model = model_class(config)
pt_model = pt_model_class(config)
tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
# Check we can load pt model in tf and vice-versa with model => model functions
tf_model = transformers.load_pytorch_model_in_tf2_model(
tf_model, pt_model, tf_inputs=tf_inputs_dict, allow_missing_keys=allow_missing_keys
)
pt_model = transformers.load_tf2_model_in_pytorch_model(
pt_model, tf_model, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
# Check we can load pt model in tf and vice-versa with checkpoint => model functions
with tempfile.TemporaryDirectory() as tmpdirname:
pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
torch.save(pt_model.state_dict(), pt_checkpoint_path)
tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(
tf_model, pt_checkpoint_path, allow_missing_keys=allow_missing_keys
)
tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
tf_model.save_weights(tf_checkpoint_path)
pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(
pt_model, tf_checkpoint_path, allow_missing_keys=allow_missing_keys
)
# Original test: check without `labels`
self.check_pt_tf_models(tf_model, pt_model, tf_inputs_dict)
@require_tf
class TFWav2Vec2UtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
self.assertListEqual(
tf.reduce_sum(mask, -1).numpy().tolist(), [mask_prob * sequence_length for _ in range(batch_size)]
)
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in tf.reduce_sum(mask, -1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
@require_tf
@slow
class TFWav2Vec2ModelIntegrationTest(unittest.TestCase):
def tearDown(self):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _load_superb(self, task, num_samples):
ds = load_dataset("anton-l/superb_dummy", task, split="test")
return ds[:num_samples]
def test_inference_ctc_normal(self):
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
input_speech = self._load_datasamples(1)
input_values = processor(input_speech, return_tensors="tf", sampling_rate=16000).input_values
logits = model(input_values).logits
predicted_ids = tf.argmax(logits, axis=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = ["a man said to the universe sir i exist"]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_normal_batched(self):
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h", do_lower_case=True)
input_speech = self._load_datasamples(2)
input_values = processor(input_speech, return_tensors="tf", padding=True, sampling_rate=16000).input_values
logits = model(input_values).logits
predicted_ids = tf.argmax(logits, axis=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight lowing cloth that was the only garment he wore",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_ctc_robust_batched(self):
model = TFWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="tf", padding=True, sampling_rate=16000)
input_values = inputs.input_values
attention_mask = inputs.attention_mask
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = tf.argmax(logits, axis=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
" him with the thousands of spectators were trivialities not worth thinking about",
"his instant panic was followed by a small sharp blow high on his chest",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm(self):
downloaded_folder = snapshot_download("patrickvonplaten/common_voice_es_sample")
file_path = glob.glob(downloaded_folder + "/*")[0]
sample = librosa.load(file_path, sr=16_000)[0]
model = TFWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(sample, return_tensors="tf").input_values
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
self.assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm_pool(self):
downloaded_folder = snapshot_download("patrickvonplaten/common_voice_es_sample")
file_path = glob.glob(downloaded_folder + "/*")[0]
sample = librosa.load(file_path, sr=16_000)[0]
model = TFWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(sample, return_tensors="tf").input_values
logits = model(input_values).logits
# test user-managed pool
with multiprocessing.get_context("fork").Pool(2) as pool:
transcription = processor.batch_decode(logits.numpy(), pool).text
self.assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
# user-managed pool + num_processes should trigger a warning
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl, multiprocessing.get_context("fork").Pool(
2
) as pool:
transcription = processor.batch_decode(logits.numpy(), pool, num_processes=2).text
self.assertIn("num_process", cl.out)
self.assertIn("it will be ignored", cl.out)
self.assertEqual(transcription[0], "el libro ha sido escrito por cervantes")
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm_invalid_pool(self):
run_test_in_subprocess(test_case=self, target_func=_test_wav2vec2_with_lm_invalid_pool, inputs=None)
def test_inference_keyword_spotting(self):
model = TFWav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ks", from_pt=True)
processor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ks")
input_data = self._load_superb("ks", 4)
inputs = processor(input_data["speech"], return_tensors="tf", padding=True)
input_values = inputs.input_values
attention_mask = inputs.attention_mask
outputs = model(input_values, attention_mask)
predicted_logits, predicted_ids = tf.math.reduce_max(outputs.logits, axis=-1), tf.argmax(
outputs.logits, axis=-1
)
expected_labels = [7, 6, 10, 9]
expected_logits = tf.convert_to_tensor([6.1186, 11.8961, 10.2931, 6.0898])
self.assertListEqual(predicted_ids.numpy().tolist(), expected_labels)
self.assertTrue(np.allclose(predicted_logits, expected_logits, atol=1e-2))
def test_inference_intent_classification(self):
model = TFWav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-ic", from_pt=True)
processor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-ic")
input_data = self._load_superb("ic", 4)
inputs = processor(input_data["speech"], return_tensors="tf", padding=True)
input_values = inputs.input_values
attention_mask = inputs.attention_mask
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits_action, predicted_ids_action = tf.math.reduce_max(outputs.logits[:, :6], axis=-1), tf.argmax(
outputs.logits[:, :6], axis=-1
)
predicted_logits_object, predicted_ids_object = tf.math.reduce_max(
outputs.logits[:, 6:20], axis=-1
), tf.argmax(outputs.logits[:, 6:20], axis=-1)
predicted_logits_location, predicted_ids_location = tf.math.reduce_max(
outputs.logits[:, 20:24], axis=-1
), tf.argmax(outputs.logits[:, 20:24], axis=-1)
expected_labels_action = [0, 0, 2, 3]
expected_logits_action = tf.convert_to_tensor([0.4568, 11.0848, 1.6621, 9.3841])
expected_labels_object = [3, 10, 3, 4]
expected_logits_object = tf.convert_to_tensor([1.5322, 10.7094, 5.2469, 22.1318])
expected_labels_location = [0, 0, 0, 1]
expected_logits_location = tf.convert_to_tensor([1.5335, 6.5096, 10.5704, 11.0569])
self.assertListEqual(predicted_ids_action.numpy().tolist(), expected_labels_action)
self.assertListEqual(predicted_ids_object.numpy().tolist(), expected_labels_object)
self.assertListEqual(predicted_ids_location.numpy().tolist(), expected_labels_location)
self.assertTrue(np.allclose(predicted_logits_action, expected_logits_action, atol=1e-2))
self.assertTrue(np.allclose(predicted_logits_object, expected_logits_object, atol=1e-2))
self.assertTrue(np.allclose(predicted_logits_location, expected_logits_location, atol=1e-2))
def test_inference_speaker_identification(self):
model = TFWav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-sid", from_pt=True)
processor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-sid")
input_data = self._load_superb("si", 4)
output_logits = []
for example in input_data["speech"]:
input = processor(example, return_tensors="tf", padding=True)
output = model(input.input_values, attention_mask=None)
output_logits.append(output.logits[0])
output_logits = tf.stack(output_logits)
predicted_logits, predicted_ids = tf.math.reduce_max(output_logits, axis=-1), tf.argmax(output_logits, axis=-1)
expected_labels = [251, 1, 1, 3]
expected_logits = tf.convert_to_tensor([37.5627, 71.6362, 64.2419, 31.7778])
self.assertListEqual(predicted_ids.numpy().tolist(), expected_labels)
self.assertTrue(np.allclose(predicted_logits, expected_logits, atol=1e-2))
def test_inference_emotion_recognition(self):
model = TFWav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er", from_pt=True)
processor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er")
input_data = self._load_superb("er", 4)
inputs = processor(input_data["speech"], return_tensors="tf", padding=True)
input_values = inputs.input_values
attention_mask = inputs.attention_mask
outputs = model(input_values, attention_mask=attention_mask)
predicted_logits, predicted_ids = tf.math.reduce_max(outputs.logits, axis=-1), tf.argmax(
outputs.logits, axis=-1
)
expected_labels = [1, 1, 2, 2]
# s3prl logits for the same batch
expected_logits = tf.convert_to_tensor([2.1722, 3.0779, 8.0287, 6.6797])
self.assertListEqual(predicted_ids.numpy().tolist(), expected_labels)
self.assertTrue(np.allclose(predicted_logits, expected_logits, atol=1e-2))
| 40,755 | 42.918103 | 128 | py |
transformers | transformers-main/tests/models/wav2vec2/test_modeling_flax_wav2vec2.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import math
import multiprocessing
import traceback
import unittest
import numpy as np
from datasets import load_dataset
from transformers import Wav2Vec2Config, is_flax_available
from transformers.testing_utils import (
CaptureLogger,
is_flaky,
is_librosa_available,
is_pt_flax_cross_test,
is_pyctcdecode_available,
require_flax,
require_librosa,
require_pyctcdecode,
require_soundfile,
run_test_in_subprocess,
slow,
)
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
import optax
from flax.traverse_util import flatten_dict
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Processor
from transformers.models.wav2vec2.modeling_flax_wav2vec2 import (
FlaxWav2Vec2ForCTC,
FlaxWav2Vec2ForPreTraining,
FlaxWav2Vec2GumbelVectorQuantizer,
FlaxWav2Vec2Model,
_compute_mask_indices,
_sample_negative_indices,
)
if is_pyctcdecode_available():
import pyctcdecode.decoder
from transformers import Wav2Vec2ProcessorWithLM
from transformers.models.wav2vec2_with_lm import processing_wav2vec2_with_lm
if is_librosa_available():
import librosa
def _test_wav2vec2_with_lm_invalid_pool(in_queue, out_queue, timeout):
error = None
try:
_ = in_queue.get(timeout=timeout)
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = librosa.resample(sample["audio"]["array"], 48_000, 16_000)
model = FlaxWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="np").input_values
logits = model(input_values).logits
# use a spawn pool, which should trigger a warning if different than fork
with CaptureLogger(pyctcdecode.decoder.logger) as cl, multiprocessing.get_context("spawn").Pool(1) as pool:
transcription = processor.batch_decode(np.array(logits), pool).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
# force batch_decode to internally create a spawn pool, which should trigger a warning if different than fork
multiprocessing.set_start_method("spawn", force=True)
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl:
transcription = processor.batch_decode(np.array(logits)).text
unittest.TestCase().assertIn("Falling back to sequential decoding.", cl.out)
unittest.TestCase().assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
except Exception:
error = f"{traceback.format_exc()}"
results = {"error": error}
out_queue.put(results, timeout=timeout)
out_queue.join()
class FlaxWav2Vec2ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=1024, # speech is longer
is_training=False,
hidden_size=24,
feat_extract_norm="layer",
feat_extract_dropout=0.0,
feat_extract_activation="gelu",
conv_dim=(32, 32, 32),
conv_stride=(4, 4, 4),
conv_kernel=(8, 8, 8),
conv_bias=False,
num_conv_pos_embeddings=16,
num_conv_pos_embedding_groups=2,
num_hidden_layers=4,
num_attention_heads=2,
hidden_dropout_prob=0.1, # this is most likely not correctly set yet
intermediate_size=20,
layer_norm_eps=1e-5,
hidden_act="gelu",
initializer_range=0.02,
vocab_size=32,
do_stable_layer_norm=True,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.hidden_size = hidden_size
self.feat_extract_norm = feat_extract_norm
self.feat_extract_dropout = feat_extract_dropout
self.feat_extract_activation = feat_extract_activation
self.conv_dim = conv_dim
self.conv_stride = conv_stride
self.conv_kernel = conv_kernel
self.conv_bias = conv_bias
self.num_conv_pos_embeddings = num_conv_pos_embeddings
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_dropout_prob = hidden_dropout_prob
self.intermediate_size = intermediate_size
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.do_stable_layer_norm = do_stable_layer_norm
self.scope = scope
output_seq_length = self.seq_length
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
output_seq_length = (output_seq_length - (kernel - 1)) / stride
self.output_seq_length = int(math.ceil(output_seq_length))
self.encoder_seq_length = self.output_seq_length
def prepare_config_and_inputs(self):
input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0)
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
config = Wav2Vec2Config(
do_stable_layer_norm=self.do_stable_layer_norm,
hidden_size=self.hidden_size,
feat_extract_norm=self.feat_extract_norm,
feat_extract_dropout=self.feat_extract_dropout,
feat_extract_activation=self.feat_extract_activation,
conv_dim=self.conv_dim,
conv_stride=self.conv_stride,
conv_kernel=self.conv_kernel,
conv_bias=self.conv_bias,
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
hidden_dropout_prob=self.hidden_dropout_prob,
intermediate_size=self.intermediate_size,
layer_norm_eps=self.layer_norm_eps,
hidden_act=self.hidden_act,
initializer_range=self.initializer_range,
vocab_size=self.vocab_size,
)
return config, input_values, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_values, attention_mask = config_and_inputs
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class FlaxWav2Vec2ModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = (
(FlaxWav2Vec2Model, FlaxWav2Vec2ForCTC, FlaxWav2Vec2ForPreTraining) if is_flax_available() else ()
)
def setUp(self):
self.model_tester = FlaxWav2Vec2ModelTester(self)
def test_train(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_values = inputs_dict["input_values"]
attention_mask = inputs_dict["attention_mask"]
model = FlaxWav2Vec2ForPreTraining(config)
features_shape = (
input_values.shape[0],
model._get_feat_extract_output_lengths(np.array(input_values.shape[1])),
)
batch_size, sequence_length = features_shape[:2]
mask_prob = 0.5
mask_length = 4
mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
dropout_rng, gumbel_rng = jax.random.split(jax.random.PRNGKey(0))
output = model(
input_values,
attention_mask=attention_mask,
mask_time_indices=mask_time_indices,
train=True,
dropout_rng=dropout_rng,
gumbel_rng=gumbel_rng,
)[0]
self.assertTrue(output.shape == (batch_size, sequence_length, model.config.proj_codevector_dim))
# overwrite because of `input_values`
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.__call__)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["input_values", "attention_mask"]
self.assertListEqual(arg_names[:2], expected_arg_names)
# overwrite because of `input_values`
def test_jit_compilation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
model = model_class(config)
@jax.jit
def model_jitted(input_values, attention_mask=None, **kwargs):
return model(input_values=input_values, attention_mask=attention_mask, **kwargs)
with self.subTest("JIT Enabled"):
jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
with self.subTest("JIT Disabled"):
with jax.disable_jit():
outputs = model_jitted(**prepared_inputs_dict).to_tuple()
self.assertEqual(len(outputs), len(jitted_outputs))
for jitted_output, output in zip(jitted_outputs, outputs):
self.assertEqual(jitted_output.shape, output.shape)
def test_freeze_feature_encoder(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
input_values = inputs_dict["input_values"]
attention_mask = inputs_dict["attention_mask"]
model = FlaxWav2Vec2ForPreTraining(config)
params = model.params
# dummy loss function
def compute_loss(
params, input_values, attention_mask, freeze_feature_encoder: bool = False, epsilon: float = 1e-8
):
outputs = model(
input_values,
attention_mask=attention_mask,
freeze_feature_encoder=freeze_feature_encoder,
params=params,
)
# compute cosine similarity of projected and projected_quantized states
cosine_sim = optax.cosine_similarity(
outputs.projected_states, outputs.projected_quantized_states, epsilon=epsilon
)
loss = cosine_sim.sum()
return loss, outputs.to_tuple()
# transform the loss function to get the gradients
grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
# compute loss, outputs and gradients for unfrozen model
(loss, outputs), grads = grad_fn(params, input_values, attention_mask, freeze_feature_encoder=False)
# compare to loss, outputs and gradients for frozen model
(loss_frozen, outputs_frozen), grads_frozen = grad_fn(
params, input_values, attention_mask, freeze_feature_encoder=True
)
# ensure that the outputs and losses remain precisely equal
for output, output_frozen in zip(outputs, outputs_frozen):
self.assertTrue((output == output_frozen).all())
self.assertEqual(loss, loss_frozen)
grads = flatten_dict(grads)
grads_frozen = flatten_dict(grads_frozen)
# ensure that the dicts of gradients contain the same keys
self.assertEqual(grads.keys(), grads_frozen.keys())
# ensure that the gradients of the feature extractor layers are precisely zero when frozen and contain non-zero entries when unfrozen
feature_extractor_grads = tuple(grads[k] for k in grads if "feature_extractor" in k)
feature_extractor_grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" in k)
for feature_extractor_grad, feature_extractor_grad_frozen in zip(
feature_extractor_grads, feature_extractor_grads_frozen
):
self.assertTrue((feature_extractor_grad_frozen == 0.0).all())
self.assertTrue((feature_extractor_grad > 0.0).any())
# ensure that the gradients of all unfrozen layers remain equal, i.e. all layers excluding the frozen 'feature_extractor'
grads = tuple(grads[k] for k in grads if "feature_extractor" not in k)
grads_frozen = tuple(grads_frozen[k] for k in grads_frozen if "feature_extractor" not in k)
for grad, grad_frozen in zip(grads, grads_frozen):
self.assertTrue((grad == grad_frozen).all())
@slow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", from_pt=True)
outputs = model(np.ones((1, 1024), dtype="f4"))
self.assertIsNotNone(outputs)
@is_pt_flax_cross_test
@is_flaky()
def test_equivalence_pt_to_flax(self):
super().test_equivalence_pt_to_flax()
@require_flax
class FlaxWav2Vec2UtilsTest(unittest.TestCase):
def test_compute_mask_indices(self):
batch_size = 4
sequence_length = 60
mask_prob = 0.5
mask_length = 1
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)])
def test_compute_mask_indices_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length)
# because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
def test_compute_mask_indices_attn_mask_overlap(self):
batch_size = 4
sequence_length = 80
mask_prob = 0.5
mask_length = 4
attention_mask = np.ones((batch_size, sequence_length), dtype=np.int32)
attention_mask[:2, sequence_length // 2 :] = 0
mask = _compute_mask_indices(
(batch_size, sequence_length), mask_prob, mask_length, attention_mask=attention_mask
)
for batch_sum in mask.sum(axis=-1):
self.assertTrue(int(batch_sum) <= mask_prob * sequence_length)
self.assertTrue(mask[:2, sequence_length // 2 :].sum() == 0)
def test_compute_perplexity(self):
probs = np.arange(100).reshape(2, 5, 10) / 100
ppl = FlaxWav2Vec2GumbelVectorQuantizer._compute_perplexity(probs)
self.assertTrue(abs(ppl.item() - 141.4291) < 1e-3)
# mask half of the input
mask = np.ones((2,), dtype=bool)
mask[0] = 0
ppl = FlaxWav2Vec2GumbelVectorQuantizer._compute_perplexity(probs, mask)
self.assertTrue(abs(ppl.item() - 58.6757) < 1e-3)
def test_sample_negatives(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
features = (np.arange(sequence_length * hidden_size) // hidden_size).reshape(
sequence_length, hidden_size
) # each value in vector consits of same value
features = np.broadcast_to(features[None, :], (batch_size, sequence_length, hidden_size))
negative_indices = _sample_negative_indices(features.shape, num_negatives)
features = features.reshape(-1, hidden_size) # BTC => (BxT)C
# take negative vectors from sampled indices
sampled_negatives = features[negative_indices.reshape(-1)]
negatives = sampled_negatives.reshape(batch_size, sequence_length, num_negatives, hidden_size).transpose(
2, 0, 1, 3
)
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features.reshape(negative.shape)) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not values of vectors
# => this means that `unique()` yields a single value for `hidden_size` dim
self.assertEqual(np.unique(negatives, axis=-1).shape, (num_negatives, batch_size, sequence_length, 1))
def test_sample_negatives_with_attn_mask(self):
batch_size = 2
sequence_length = 10
hidden_size = 4
num_negatives = 3
features = (np.arange(sequence_length * hidden_size) // hidden_size).reshape(
sequence_length, hidden_size
) # each value in vector consits of same value
# second half of last input tensor is padded
attention_mask = np.ones((batch_size, sequence_length), dtype=np.int8)
attention_mask[-1, sequence_length // 2 :] = 0
forbidden_indices = (
np.arange(sequence_length // 2, sequence_length, dtype=np.int32) + (batch_size - 1) * sequence_length
).tolist()
features = np.broadcast_to(features[None, :], (batch_size, sequence_length, hidden_size))
negative_indices = _sample_negative_indices(features.shape, num_negatives, attention_mask=attention_mask)
# make sure that no padding tokens are sampled
self.assertTrue(all(idx not in negative_indices for idx in forbidden_indices))
features = features.reshape(-1, hidden_size) # BTC => (BxT)C
# take negative vectors from sampled indices
sampled_negatives = features[negative_indices.reshape(-1)]
negatives = sampled_negatives.reshape(batch_size, sequence_length, num_negatives, hidden_size).transpose(
2, 0, 1, 3
)
self.assertTrue(negatives.shape == (num_negatives, batch_size, sequence_length, hidden_size))
# make sure no negatively sampled vector is actually a positive one
for negative in negatives:
self.assertTrue(((negative - features.reshape(negative.shape)) == 0).sum() == 0.0)
# make sure that full vectors are sampled and not just slices of vectors
# => this means that `unique()` yields a single value for `hidden_size` dim
self.assertEqual(np.unique(negatives, axis=-1).shape, (num_negatives, batch_size, sequence_length, 1))
@require_flax
@require_soundfile
@slow
class FlaxWav2Vec2ModelIntegrationTest(unittest.TestCase):
def _load_datasamples(self, num_samples):
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# automatic decoding with librispeech
speech_samples = ds.sort("id").filter(
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
)[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def test_inference_ctc_robust_batched(self):
model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", from_pt=True)
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self", do_lower_case=True)
input_speech = self._load_datasamples(4)
inputs = processor(input_speech, return_tensors="np", padding=True)
input_values = inputs.input_values
attention_mask = inputs.attention_mask
logits = model(input_values, attention_mask=attention_mask).logits
predicted_ids = jnp.argmax(logits, axis=-1)
predicted_trans = processor.batch_decode(predicted_ids)
EXPECTED_TRANSCRIPTIONS = [
"a man said to the universe sir i exist",
"sweat covered brion's body trickling into the tight loin cloth that was the only garment he wore",
"the cut on his chest still dripping blood the ache of his overstrained eyes even the soaring arena around"
" him with the thousands of spectators were trivialities not worth thinking about",
"his instant panic was followed by a small sharp blow high on his chest",
]
self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
def test_inference_pretrained(self):
model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60", from_pt=True)
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
"facebook/wav2vec2-large-lv60", return_attention_mask=True
)
input_speech = self._load_datasamples(2)
inputs_dict = feature_extractor(input_speech, return_tensors="np", padding=True)
features_shape = (
inputs_dict["input_values"].shape[0],
model._get_feat_extract_output_lengths(np.array(inputs_dict["input_values"].shape[1])),
)
mask_time_indices = _compute_mask_indices(
features_shape,
model.config.mask_time_prob,
model.config.mask_time_length,
min_masks=2,
)
outputs = model(
inputs_dict.input_values,
attention_mask=inputs_dict.attention_mask,
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim = optax.cosine_similarity(
outputs.projected_states, outputs.projected_quantized_states, epsilon=1e-8
)
# retrieve cosine sim of masked features
cosine_sim_masked = cosine_sim[mask_time_indices]
# ... now compare to randomly initialized model
config = Wav2Vec2Config.from_pretrained("facebook/wav2vec2-large-lv60")
model_rand = FlaxWav2Vec2ForPreTraining(config)
outputs_rand = model_rand(
inputs_dict.input_values,
attention_mask=inputs_dict.attention_mask,
mask_time_indices=mask_time_indices,
)
# compute cosine similarity
cosine_sim_rand = optax.cosine_similarity(
outputs_rand.projected_states, outputs_rand.projected_quantized_states
)
# retrieve cosine sim of masked features
cosine_sim_masked_rand = cosine_sim_rand[mask_time_indices]
# a pretrained wav2vec2 model has learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states > 0.5
# a random wav2vec2 model has not learned to predict the quantized latent states
# => the cosine similarity between quantized states and predicted states is very likely < 0.1
self.assertTrue(cosine_sim_masked.mean().item() - 5 * cosine_sim_masked_rand.mean().item() > 0)
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = librosa.resample(sample["audio"]["array"], 48_000, 16_000)
model = FlaxWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="np").input_values
logits = model(input_values).logits
transcription = processor.batch_decode(np.array(logits)).text
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm_pool(self):
ds = load_dataset("common_voice", "es", split="test", streaming=True)
sample = next(iter(ds))
resampled_audio = librosa.resample(sample["audio"]["array"], 48_000, 16_000)
model = FlaxWav2Vec2ForCTC.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm")
input_values = processor(resampled_audio, return_tensors="np").input_values
logits = model(input_values).logits
# test user-managed pool
with multiprocessing.get_context("fork").Pool(2) as pool:
transcription = processor.batch_decode(np.array(logits), pool).text
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
# user-managed pool + num_processes should trigger a warning
with CaptureLogger(processing_wav2vec2_with_lm.logger) as cl, multiprocessing.get_context("fork").Pool(
2
) as pool:
transcription = processor.batch_decode(np.array(logits), pool, num_processes=2).text
self.assertIn("num_process", cl.out)
self.assertIn("it will be ignored", cl.out)
self.assertEqual(transcription[0], "bien y qué regalo vas a abrir primero")
@require_pyctcdecode
@require_librosa
def test_wav2vec2_with_lm_invalid_pool(self):
run_test_in_subprocess(test_case=self, target_func=_test_wav2vec2_with_lm_invalid_pool, inputs=None)
| 26,291 | 40.08125 | 141 | py |
transformers | transformers-main/tests/models/gptj/test_modeling_gptj.py | # coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import unittest
from transformers import GPTJConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, tooslow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoTokenizer,
GPTJForCausalLM,
GPTJForQuestionAnswering,
GPTJForSequenceClassification,
GPTJModel,
)
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_12
else:
is_torch_greater_or_equal_than_1_12 = False
class GPTJModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_input_mask=True,
use_labels=True,
use_mc_token_ids=True,
vocab_size=99,
hidden_size=32,
rotary_dim=4,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_input_mask = use_input_mask
self.use_labels = use_labels
self.use_mc_token_ids = use_mc_token_ids
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.rotary_dim = rotary_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
def get_large_model_config(self):
return GPTJConfig.from_pretrained("EleutherAI/gpt-j-6B")
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
mc_token_ids = None
if self.use_mc_token_ids:
mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
)
def get_config(self):
return GPTJConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
use_cache=True,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
rotary_dim=self.rotary_dim,
)
def get_pipeline_config(self):
config = self.get_config()
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_gptj_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTJModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(len(result.past_key_values), config.n_layer)
def create_and_check_gptj_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTJModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True)
outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids)
outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False)
self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf))
self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"]
output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gptj_model_attention_mask_past(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPTJModel(config=config)
model.to(torch_device)
model.eval()
# create attention mask
attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
half_seq_length = self.seq_length // 2
attn_mask[:, half_seq_length:] = 0
# first forward pass
output, past = model(input_ids, attention_mask=attn_mask).to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size)
# change a random masked slice from input_ids
random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1
random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1)
input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens
# append to next input_ids and attn_mask
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
attn_mask = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
dim=1,
)
# get two different outputs
output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"]
output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["last_hidden_state"]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_gptj_model_past_large_inputs(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args
):
model = GPTJModel(config=config)
model.to(torch_device)
model.eval()
# first forward pass
outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True)
output, past = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and token_type_ids
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask
)["last_hidden_state"]
output_from_past = model(
next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past
)["last_hidden_state"]
self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTJForCausalLM(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_forward_and_backwards(
self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False
):
model = GPTJForCausalLM(config)
if gradient_checkpointing:
model.gradient_checkpointing_enable()
model.to(torch_device)
result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
result.loss.backward()
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
input_mask,
head_mask,
token_type_ids,
mc_token_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
return config, inputs_dict
@require_torch
class GPTJModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(GPTJModel, GPTJForCausalLM, GPTJForSequenceClassification, GPTJForQuestionAnswering)
if is_torch_available()
else ()
)
all_generative_model_classes = (GPTJForCausalLM,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": GPTJModel,
"question-answering": GPTJForQuestionAnswering,
"text-classification": GPTJForSequenceClassification,
"text-generation": GPTJForCausalLM,
"zero-shot": GPTJForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
test_pruning = False
test_missing_keys = False
test_model_parallel = False
test_head_masking = False
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_12, reason="PR #22069 made changes that require torch v1.12+."
)
def test_torch_fx(self):
super().test_torch_fx()
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_12, reason="PR #22069 made changes that require torch v1.12+."
)
def test_torch_fx_output_loss(self):
super().test_torch_fx_output_loss()
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast")
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
# special case for DoubleHeads model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
return inputs_dict
def setUp(self):
self.model_tester = GPTJModelTester(self)
self.config_tester = ConfigTester(self, config_class=GPTJConfig, n_embd=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_gptj_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model(*config_and_inputs)
def test_gptj_model_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_past(*config_and_inputs)
def test_gptj_model_att_mask_past(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_attention_mask_past(*config_and_inputs)
def test_gptj_model_past_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_gptj_model_past_large_inputs(*config_and_inputs)
def test_gptj_lm_head_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*config_and_inputs)
def test_gptj_gradient_checkpointing(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)
@tooslow
def test_batch_generation(self):
# Marked as @tooslow due to GPU OOM
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16)
model.to(torch_device)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
tokenizer.padding_side = "left"
# Define PAD Token = EOS Token = 50256
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = model.config.eos_token_id
# use different length sentences to test batching
sentences = [
"Hello, my dog is a little",
"Today, I",
]
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
input_ids = inputs["input_ids"].to(torch_device)
token_type_ids = torch.cat(
[
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
input_ids.new_full((input_ids.shape[0], 1), 500),
],
dim=-1,
)
outputs = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
)
outputs_tt = model.generate(
input_ids=input_ids,
attention_mask=inputs["attention_mask"].to(torch_device),
token_type_ids=token_type_ids,
)
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
output_non_padded = model.generate(input_ids=inputs_non_padded)
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
expected_output_sentence = [
"Hello, my dog is a little over a year old and has been diagnosed with a heart murmur",
"Today, I’m going to talk about the most important thing in the",
]
self.assertListEqual(expected_output_sentence, batch_out_sentence)
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
@slow
def test_model_from_pretrained(self):
for model_name in GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = GPTJModel.from_pretrained(model_name, revision="float16", torch_dtype=torch.float16)
self.assertIsNotNone(model)
@require_torch
class GPTJModelLanguageGenerationTest(unittest.TestCase):
@tooslow
def test_lm_generate_gptj(self):
# Marked as @tooslow due to GPU OOM
for checkpointing in [True, False]:
model = GPTJForCausalLM.from_pretrained(
"EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16
)
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(torch_device)
input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog
# fmt: off
# The dog is a man's best friend. It is a loyal companion, and it is a friend
expected_output_ids = [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
# fmt: on
output_ids = model.generate(input_ids, do_sample=False)
self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@tooslow
def test_gptj_sample(self):
# Marked as @tooslow due to GPU OOM (issue #13676)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B", revision="float16")
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16)
model.to(torch_device)
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
input_ids = tokenized.input_ids.to(torch_device)
output_ids = model.generate(input_ids, do_sample=True)
output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
token_type_ids = tokenized.token_type_ids.to(torch_device)
output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5)
output_seq_tt = model.generate(
input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5
)
output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
if torch_device == "cuda":
EXPECTED_OUTPUT_STR = (
"Today is a nice day and I've already been enjoying it. I walked to work with my wife"
)
else:
EXPECTED_OUTPUT_STR = "Today is a nice day and one of those days that feels a bit more alive. I am ready"
self.assertEqual(output_str, EXPECTED_OUTPUT_STR)
self.assertTrue(
all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs)))
) # token_type_ids should change output
@slow
def test_gptj_sample_max_time(self):
tokenizer = AutoTokenizer.from_pretrained("anton-l/gpt-j-tiny-random")
model = GPTJForCausalLM.from_pretrained("anton-l/gpt-j-tiny-random")
model.to(torch_device)
torch.manual_seed(0)
tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
input_ids = tokenized.input_ids.to(torch_device)
MAX_TIME = 0.5
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME))
self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
start = datetime.datetime.now()
model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
duration = datetime.datetime.now() - start
self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME))
@tooslow
def test_contrastive_search_gptj(self):
article = (
"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and "
"research laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based"
)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = GPTJForCausalLM.from_pretrained(
"EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16
).to(torch_device)
input_ids = tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
outputs = model.generate(input_ids, penalty_alpha=0.6, top_k=4, max_length=256)
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
self.assertListEqual(
generated_text,
[
"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research "
"laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, "
"United Kingdom with offices in Mountain View, San Francisco, New York City, Paris, Tokyo, Seoul, "
"Beijing, Singapore, Tel Aviv, Dublin, Sydney, and Melbourne.[1]\n\nContents\n\nIn 2010, Google's "
"parent company, Alphabet, announced a $500 million investment in DeepMind, with the aim of creating "
"a company that would apply deep learning to problems in healthcare, energy, transportation, and "
"other areas.[2]\n\nOn April 23, 2014, Google announced that it had acquired DeepMind for $400 "
"million in cash and stock.[3] The acquisition was seen as a way for Google to enter the "
"fast-growing field of artificial intelligence (AI), which it had so far avoided due to concerns "
'about ethical and social implications.[4] Google co-founder Sergey Brin said that he was "thrilled" '
'to have acquired DeepMind, and that it would "help us push the boundaries of AI even further."'
"[5]\n\nDeepMind's founders, Demis Hassabis and Mustafa Suleyman, were joined by a number of Google "
"employees"
],
)
| 28,367 | 42.509202 | 139 | py |
transformers | transformers-main/tests/models/gptj/test_modeling_flax_gptj.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPT2Tokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class FlaxGPTJModelTester:
def __init__(
self,
parent,
batch_size=14,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=False,
use_labels=True,
vocab_size=99,
hidden_size=32,
rotary_dim=4,
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
initializer_range=0.02,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.rotary_dim = rotary_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.initializer_range = initializer_range
self.scope = None
self.bos_token_id = vocab_size - 1
self.eos_token_id = vocab_size - 1
self.pad_token_id = vocab_size - 1
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
config = GPTJConfig(
vocab_size=self.vocab_size,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
n_positions=self.max_position_embeddings,
use_cache=False,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
rotary_dim=self.rotary_dim,
)
return (config, input_ids, input_mask)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
max_decoder_length = 20
model = model_class_name(config)
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
attention_mask=attention_mask,
past_key_values=outputs_cache.past_key_values,
position_ids=position_ids,
)
outputs = model(input_ids)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask):
max_decoder_length = 20
model = model_class_name(config)
attention_mask_cache = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
axis=-1,
)
past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
position_ids = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
)
outputs_cache = model(
input_ids[:, :-1],
attention_mask=attention_mask_cache,
past_key_values=past_key_values,
position_ids=position_ids,
)
position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
outputs_cache_next = model(
input_ids[:, -1:],
past_key_values=outputs_cache.past_key_values,
attention_mask=attention_mask_cache,
position_ids=position_ids,
)
outputs = model(input_ids, attention_mask=attention_mask)
diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
@require_flax
class FlaxGPTJModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
all_model_classes = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
all_generative_model_classes = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def setUp(self):
self.model_tester = FlaxGPTJModelTester(self)
def test_use_cache_forward(self):
for model_class_name in self.all_model_classes:
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
def test_use_cache_forward_with_attn_mask(self):
for model_class_name in self.all_model_classes:
config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
model_class_name, config, input_ids, attention_mask
)
@tooslow
def test_batch_generation(self):
tokenizer = GPT2Tokenizer.from_pretrained("gpt2", pad_token="<|endoftext|>", padding_side="left")
inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True)
model = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
model.do_sample = False
model.config.pad_token_id = model.config.eos_token_id
jit_generate = jax.jit(model.generate)
output_sequences = jit_generate(
inputs["input_ids"], attention_mask=inputs["attention_mask"], pad_token_id=tokenizer.pad_token_id
).sequences
output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True)
expected_string = [
"Hello this is a long string of text.\n\nI'm trying to get the text of the",
"Hey, I'm a little late to the party. I'm going to",
]
self.assertListEqual(output_string, expected_string)
# overwrite from common since `attention_mask` in combination
# with `causal_mask` behaves slighly differently
@is_pt_flax_cross_test
def test_equivalence_pt_to_flax(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
batch_size, seq_length = pt_inputs["input_ids"].shape
rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
pt_inputs["attention_mask"][batch_idx, :start_index] = 0
pt_inputs["attention_mask"][batch_idx, start_index:] = 1
prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
pt_model = pt_model_class(config).eval()
fx_model = model_class(config, dtype=jnp.float32)
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
fx_model.params = fx_state
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
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[:, -1], pt_output[:, -1].numpy(), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
self.assertEqual(
len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
)
for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
self.assert_almost_equals(fx_output_loaded[:, -1], pt_output[:, -1].numpy(), 4e-2)
# overwrite from common since `attention_mask` in combination
# with `causal_mask` behaves slighly differently
@is_pt_flax_cross_test
def test_equivalence_flax_to_pt(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__):
# prepare inputs
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
pt_model_class = getattr(transformers, pt_model_class_name)
pt_model = pt_model_class(config).eval()
fx_model = model_class(config, dtype=jnp.float32)
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
batch_size, seq_length = pt_inputs["input_ids"].shape
rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,))
for batch_idx, start_index in enumerate(rnd_start_indices):
pt_inputs["attention_mask"][batch_idx, :start_index] = 0
pt_inputs["attention_mask"][batch_idx, start_index:] = 1
prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0
prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
pt_outputs = pt_model(**pt_inputs).to_tuple()
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
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[:, -1], pt_output[:, -1].numpy(), 4e-2)
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(tmpdirname)
pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
with torch.no_grad():
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
self.assertEqual(
len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
)
for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded):
self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2)
@tooslow
def test_model_from_pretrained(self):
for model_class_name in self.all_model_classes:
model = model_class_name.from_pretrained("EleutherAI/gpt-j-6B")
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
| 14,519 | 43.133739 | 118 | py |
transformers | transformers-main/tests/models/dinat/test_modeling_dinat.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch Dinat model. """
import collections
import inspect
import unittest
from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import DinatBackbone, DinatForImageClassification, DinatModel
from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class DinatModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
patch_size=4,
num_channels=3,
embed_dim=16,
depths=[1, 2, 1],
num_heads=[2, 4, 8],
kernel_size=3,
dilations=[[3], [1, 2], [1]],
mlp_ratio=2.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
patch_norm=True,
initializer_range=0.02,
layer_norm_eps=1e-5,
is_training=True,
scope=None,
use_labels=True,
num_labels=10,
out_features=["stage1", "stage2"],
out_indices=[1, 2],
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_heads = num_heads
self.kernel_size = kernel_size
self.dilations = dilations
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.patch_norm = patch_norm
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.is_training = is_training
self.scope = scope
self.use_labels = use_labels
self.num_labels = num_labels
self.out_features = out_features
self.out_indices = out_indices
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return DinatConfig(
num_labels=self.num_labels,
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
embed_dim=self.embed_dim,
depths=self.depths,
num_heads=self.num_heads,
kernel_size=self.kernel_size,
dilations=self.dilations,
mlp_ratio=self.mlp_ratio,
qkv_bias=self.qkv_bias,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
drop_path_rate=self.drop_path_rate,
hidden_act=self.hidden_act,
patch_norm=self.patch_norm,
layer_norm_eps=self.layer_norm_eps,
initializer_range=self.initializer_range,
out_features=self.out_features,
out_indices=self.out_indices,
)
def create_and_check_model(self, config, pixel_values, labels):
model = DinatModel(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
expected_height = expected_width = (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1))
expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1))
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, expected_height, expected_width, expected_dim)
)
def create_and_check_for_image_classification(self, config, pixel_values, labels):
model = DinatForImageClassification(config)
model.to(torch_device)
model.eval()
result = model(pixel_values, labels=labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
# test greyscale images
config.num_channels = 1
model = DinatForImageClassification(config)
model.to(torch_device)
model.eval()
pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
result = model(pixel_values)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_backbone(self, config, pixel_values, labels):
model = DinatBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify hidden states
self.parent.assertEqual(len(result.feature_maps), len(config.out_features))
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], 16, 16])
# verify channels
self.parent.assertEqual(len(model.channels), len(config.out_features))
# verify backbone works with out_features=None
config.out_features = None
model = DinatBackbone(config=config)
model.to(torch_device)
model.eval()
result = model(pixel_values)
# verify feature maps
self.parent.assertEqual(len(result.feature_maps), 1)
self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4])
# verify channels
self.parent.assertEqual(len(model.channels), 1)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_natten
@require_torch
class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
DinatModel,
DinatForImageClassification,
DinatBackbone,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
if is_torch_available()
else {}
)
fx_compatible = False
test_torchscript = False
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
def setUp(self):
self.model_tester = DinatModelTester(self)
self.config_tester = ConfigTester(self, config_class=DinatConfig, embed_dim=37)
def test_config(self):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def create_and_test_config_common_properties(self):
return
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_image_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
def test_backbone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*config_and_inputs)
@unittest.skip(reason="Dinat does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip(reason="Dinat does not use feedforward chunking")
def test_feed_forward_chunking(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, nn.Linear))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
self.skipTest("Dinat's attention operation is handled entirely by NATTEN.")
def check_hidden_states_output(self, inputs_dict, config, model_class, image_size):
model = model_class(config)
model.to(torch_device)
model.eval()
with torch.no_grad():
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = getattr(
self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1
)
self.assertEqual(len(hidden_states), expected_num_layers)
# Dinat has a different seq_length
patch_size = (
config.patch_size
if isinstance(config.patch_size, collections.abc.Iterable)
else (config.patch_size, config.patch_size)
)
height = image_size[0] // patch_size[0]
width = image_size[1] // patch_size[1]
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[height, width, self.model_tester.embed_dim],
)
if model_class.__name__ != "DinatBackbone":
reshaped_hidden_states = outputs.reshaped_hidden_states
self.assertEqual(len(reshaped_hidden_states), expected_num_layers)
batch_size, num_channels, height, width = reshaped_hidden_states[0].shape
reshaped_hidden_states = (
reshaped_hidden_states[0].view(batch_size, num_channels, height, width).permute(0, 2, 3, 1)
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-3:]),
[height, width, self.model_tester.embed_dim],
)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
image_size = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size, collections.abc.Iterable)
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
self.check_hidden_states_output(inputs_dict, config, model_class, image_size)
@slow
def test_model_from_pretrained(self):
for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = DinatModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
@require_natten
@require_vision
@require_torch
class DinatModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(torch_device)
image_processor = self.default_image_processor
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
# forward pass
with torch.no_grad():
outputs = model(**inputs)
# verify the logits
expected_shape = torch.Size((1, 1000))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = torch.tensor([-0.1545, -0.7667, 0.4642]).to(torch_device)
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
@require_torch
@require_natten
class DinatBackboneTest(unittest.TestCase, BackboneTesterMixin):
all_model_classes = (DinatBackbone,) if is_torch_available() else ()
config_class = DinatConfig
def setUp(self):
self.model_tester = DinatModelTester(self)
| 15,095 | 36.74 | 118 | py |
transformers | transformers-main/tests/models/bert/test_modeling_flax_bert.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.bert.modeling_flax_bert import (
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
)
class FlaxBertModelTester(unittest.TestCase):
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_attention_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_choices=4,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_attention_mask = use_attention_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_choices = num_choices
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
attention_mask = None
if self.use_attention_mask:
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
config = BertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
return config, input_ids, token_type_ids, attention_mask
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config_and_inputs = self.prepare_config_and_inputs()
config, input_ids, token_type_ids, attention_mask = config_and_inputs
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class FlaxBertModelTest(FlaxModelTesterMixin, unittest.TestCase):
test_head_masking = True
all_model_classes = (
(
FlaxBertModel,
FlaxBertForPreTraining,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForQuestionAnswering,
FlaxBertForNextSentencePrediction,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def setUp(self):
self.model_tester = FlaxBertModelTester(self)
@slow
def test_model_from_pretrained(self):
# Only check this for base model, not necessary for all model classes.
# This will also help speed-up tests.
model = FlaxBertModel.from_pretrained("bert-base-cased")
outputs = model(np.ones((1, 1)))
self.assertIsNotNone(outputs)
| 5,927 | 35.146341 | 114 | py |
transformers | transformers-main/tests/models/bert/test_modeling_bert.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
from transformers import BertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import CaptureLogger, require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLMHeadModel,
BertModel,
logging,
)
from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST
class BertModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_input_mask=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_input_mask = use_input_mask
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_mask = None
if self.use_input_mask:
input_mask = random_attention_mask([self.batch_size, self.seq_length])
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def get_config(self):
"""
Returns a tiny configuration by default.
"""
return BertConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
is_decoder=False,
initializer_range=self.initializer_range,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = BertModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = BertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_model_for_causal_lm_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = BertLMHeadModel(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
encoder_hidden_states=encoder_hidden_states,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_decoder_model_past_large_inputs(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.is_decoder = True
config.add_cross_attention = True
model = BertLMHeadModel(config=config).to(torch_device).eval()
# first forward pass
outputs = model(
input_ids,
attention_mask=input_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
use_cache=True,
)
past_key_values = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
output_from_no_past = model(
next_input_ids,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_hidden_states=True,
)["hidden_states"][0]
output_from_past = model(
next_tokens,
attention_mask=next_attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_hidden_states=True,
)["hidden_states"][0]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
def create_and_check_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = BertForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = BertForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = BertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class BertModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
BertModel,
BertLMHeadModel,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (BertLMHeadModel,) if is_torch_available() else ()
pipeline_model_mapping = (
{
"feature-extraction": BertModel,
"fill-mask": BertForMaskedLM,
"question-answering": BertForQuestionAnswering,
"text-classification": BertForSequenceClassification,
"text-generation": BertLMHeadModel,
"token-classification": BertForTokenClassification,
"zero-shot": BertForSequenceClassification,
}
if is_torch_available()
else {}
)
fx_compatible = True
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
def setUp(self):
self.model_tester = BertModelTester(self)
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_various_embeddings(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
config_and_inputs[0].position_embedding_type = type
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_decoder_model_past_with_large_inputs_relative_pos_emb(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
config_and_inputs[0].position_embedding_type = "relative_key"
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_warning_if_padding_and_no_attention_mask(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.model_tester.prepare_config_and_inputs()
# Set pad tokens in the input_ids
input_ids[0, 0] = config.pad_token_id
# Check for warnings if the attention_mask is missing.
logger = logging.get_logger("transformers.modeling_utils")
# clear cache so we can test the warning is emitted (from `warning_once`).
logger.warning_once.cache_clear()
with CaptureLogger(logger) as cl:
model = BertModel(config=config)
model.to(torch_device)
model.eval()
model(input_ids, attention_mask=None, token_type_ids=token_type_ids)
self.assertIn("We strongly recommend passing in an `attention_mask`", cl.out)
@slow
def test_model_from_pretrained(self):
for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = BertModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@slow
@require_torch_gpu
def test_torchscript_device_change(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == BertForMultipleChoice:
return
config.torchscript = True
model = model_class(config=config)
inputs_dict = self._prepare_for_class(inputs_dict, model_class)
traced_model = torch.jit.trace(
model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
)
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(traced_model, os.path.join(tmp, "bert.pt"))
loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device)
loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
@require_torch
class BertModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head_absolute_embedding(self):
model = BertModel.from_pretrained("bert-base-uncased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
@slow
def test_inference_no_head_relative_embedding_key(self):
model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
@slow
def test_inference_no_head_relative_embedding_key_query(self):
model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]]
)
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
| 27,037 | 39.355224 | 119 | py |
transformers | transformers-main/tests/models/bert/test_tokenization_bert_tf.py | import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
TOKENIZER_CHECKPOINTS = ["bert-base-uncased", "bert-base-cased"]
TINY_MODEL_CHECKPOINT = "hf-internal-testing/tiny-bert-tf-only"
if is_tf_available():
class ModelToSave(tf.keras.Model):
def __init__(self, tokenizer):
super().__init__()
self.tokenizer = tokenizer
config = AutoConfig.from_pretrained(TINY_MODEL_CHECKPOINT)
self.bert = TFAutoModel.from_config(config)
def call(self, inputs):
tokenized = self.tokenizer(inputs)
out = self.bert(**tokenized)
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class BertTokenizationTest(unittest.TestCase):
# The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,
# so that's what we focus on here.
def setUp(self):
super().setUp()
self.tokenizers = [
BertTokenizer.from_pretrained(checkpoint) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
self.tf_tokenizers = [TFBertTokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(checkpoint, use_fast_bert_tokenizer=False)
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers) == len(self.tf_tokenizers)
self.test_sentences = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
self.paired_sentences = list(zip(self.test_sentences, self.test_sentences[::-1]))
def test_output_equivalence(self):
for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers):
for test_inputs in (self.test_sentences, self.paired_sentences):
python_outputs = tokenizer(test_inputs, return_tensors="tf", padding="longest")
tf_outputs = tf_tokenizer(test_inputs)
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key], tf.int64) == tf_outputs[key]))
@slow
def test_different_pairing_styles(self):
for tf_tokenizer in self.tf_tokenizers:
merged_outputs = tf_tokenizer(self.paired_sentences)
separated_outputs = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences],
text_pair=[sentence[1] for sentence in self.paired_sentences],
)
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key], tf.int64) == separated_outputs[key]))
@slow
def test_graph_mode(self):
for tf_tokenizer in self.tf_tokenizers:
compiled_tokenizer = tf.function(tf_tokenizer)
for test_inputs in (self.test_sentences, self.paired_sentences):
test_inputs = tf.constant(test_inputs)
compiled_outputs = compiled_tokenizer(test_inputs)
eager_outputs = tf_tokenizer(test_inputs)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def test_saved_model(self):
for tf_tokenizer in self.tf_tokenizers:
model = ModelToSave(tokenizer=tf_tokenizer)
test_inputs = tf.convert_to_tensor(self.test_sentences)
out = model(test_inputs) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
save_path = Path(tempdir) / "saved.model"
model.save(save_path)
loaded_model = tf.keras.models.load_model(save_path)
loaded_output = loaded_model(test_inputs)
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)), 1e-5)
| 4,893 | 43.899083 | 118 | py |
transformers | transformers-main/tests/models/fnet/test_modeling_fnet.py | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch FNet model. """
import unittest
from typing import Dict, List, Tuple
from transformers import FNetConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetModel,
FNetTokenizerFast,
)
from transformers.models.fnet.modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetBasicFourierTransform,
is_scipy_available,
)
# Override ConfigTester
class FNetConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
if self.has_text_modality:
self.parent.assertTrue(hasattr(config, "vocab_size"))
self.parent.assertTrue(hasattr(config, "hidden_size"))
self.parent.assertTrue(hasattr(config, "num_hidden_layers"))
class FNetModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
hidden_size=32,
num_hidden_layers=5,
intermediate_size=37,
hidden_act="gelu",
hidden_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
num_choices=4,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
token_type_ids = None
if self.use_token_type_ids:
token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
sequence_labels = None
token_labels = None
choice_labels = None
if self.use_labels:
sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
choice_labels = ids_tensor([self.batch_size], self.num_choices)
config = self.get_config()
return config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
def get_config(self):
return FNetConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
intermediate_size=self.intermediate_size,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
max_position_embeddings=self.max_position_embeddings,
type_vocab_size=self.type_vocab_size,
initializer_range=self.initializer_range,
tpu_short_seq_length=self.seq_length,
)
@require_torch
def create_and_check_fourier_transform(self, config):
hidden_states = floats_tensor([self.batch_size, self.seq_length, config.hidden_size])
transform = FNetBasicFourierTransform(config)
fftn_output = transform(hidden_states)
config.use_tpu_fourier_optimizations = True
if is_scipy_available():
transform = FNetBasicFourierTransform(config)
dft_output = transform(hidden_states)
config.max_position_embeddings = 4097
transform = FNetBasicFourierTransform(config)
fft_output = transform(hidden_states)
if is_scipy_available():
self.parent.assertTrue(torch.allclose(fftn_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fft_output[0][0], dft_output[0][0], atol=1e-4))
self.parent.assertTrue(torch.allclose(fftn_output[0][0], fft_output[0][0], atol=1e-4))
def create_and_check_model(self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels):
model = FNetModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForPreTraining(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
labels=token_labels,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForMaskedLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_next_sentence_prediction(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
model = FNetForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = FNetForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = FNetForTokenClassification(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = FNetForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
token_type_ids=multiple_choice_token_type_ids,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(
config,
input_ids,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
) = config_and_inputs
inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids}
return config, inputs_dict
@require_torch
class FNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(
FNetModel,
FNetForPreTraining,
FNetForMaskedLM,
FNetForNextSentencePrediction,
FNetForMultipleChoice,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": FNetModel,
"fill-mask": FNetForMaskedLM,
"question-answering": FNetForQuestionAnswering,
"text-classification": FNetForSequenceClassification,
"token-classification": FNetForTokenClassification,
"zero-shot": FNetForSequenceClassification,
}
if is_torch_available()
else {}
)
# Skip Tests
test_pruning = False
test_head_masking = False
test_pruning = False
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name == "QAPipelineTests" and not tokenizer_name.endswith("Fast"):
return True
return False
# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
if return_labels:
if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
inputs_dict["labels"] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
)
inputs_dict["next_sentence_label"] = torch.zeros(
self.model_tester.batch_size, dtype=torch.long, device=torch_device
)
return inputs_dict
# Overriden Tests
def test_attention_outputs(self):
pass
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(t):
t[t != t] = 0
return t
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
with torch.no_grad():
tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif isinstance(tuple_object, Dict):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values(), dict_object.values()
):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
model.to(torch_device)
model.eval()
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs)
# tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
# dict_inputs = self._prepare_for_class(inputs_dict, model_class)
# check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
def test_retain_grad_hidden_states_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
config.output_attentions = True
# no need to test all models as different heads yield the same functionality
model_class = self.all_model_classes[0]
model = model_class(config)
model.to(torch_device)
inputs = self._prepare_for_class(inputs_dict, model_class)
outputs = model(**inputs)
output = outputs[0]
hidden_states = outputs.hidden_states[0]
hidden_states.retain_grad()
output.flatten()[0].backward(retain_graph=True)
self.assertIsNotNone(hidden_states.grad)
def setUp(self):
self.model_tester = FNetModelTester(self)
self.config_tester = FNetConfigTester(self, config_class=FNetConfig, hidden_size=37)
def test_config(self):
self.config_tester.run_common_tests()
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):
for model_name in FNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = FNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
@require_torch
class FNetModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_for_masked_lm(self):
"""
For comparison:
1. Modify the pre-training model `__call__` to skip computing metrics and return masked_lm_output like so:
```
...
sequence_output, pooled_output = EncoderModel(
self.config, random_seed=self.random_seed, name="encoder")(
input_ids, input_mask, type_ids, deterministic=deterministic)
masked_lm_output = nn.Dense(
self.config.d_emb,
kernel_init=default_kernel_init,
name="predictions_dense")(
sequence_output)
masked_lm_output = nn.gelu(masked_lm_output)
masked_lm_output = nn.LayerNorm(
epsilon=LAYER_NORM_EPSILON, name="predictions_layer_norm")(
masked_lm_output)
masked_lm_logits = layers.OutputProjection(
kernel=self._get_embedding_table(), name="predictions_output")(
masked_lm_output)
next_sentence_logits = layers.OutputProjection(
n_out=2, kernel_init=default_kernel_init, name="classification")(
pooled_output)
return masked_lm_logits
...
```
2. Run the following:
>>> import jax.numpy as jnp
>>> import sentencepiece as spm
>>> from flax.training import checkpoints
>>> from f_net.models import PreTrainingModel
>>> from f_net.configs.pretraining import get_config, ModelArchitecture
>>> pretrained_params = checkpoints.restore_checkpoint('./f_net/f_net_checkpoint', None) # Location of original checkpoint
>>> pretrained_config = get_config()
>>> pretrained_config.model_arch = ModelArchitecture.F_NET
>>> vocab_filepath = "./f_net/c4_bpe_sentencepiece.model" # Location of the sentence piece model
>>> tokenizer = spm.SentencePieceProcessor()
>>> tokenizer.Load(vocab_filepath)
>>> with pretrained_config.unlocked():
>>> pretrained_config.vocab_size = tokenizer.GetPieceSize()
>>> tokens = jnp.array([[0, 1, 2, 3, 4, 5]])
>>> type_ids = jnp.zeros_like(tokens, dtype="i4")
>>> attention_mask = jnp.ones_like(tokens) # Dummy. This gets deleted inside the model.
>>> flax_pretraining_model = PreTrainingModel(pretrained_config)
>>> pretrained_model_params = freeze(pretrained_params['target'])
>>> flax_model_outputs = flax_pretraining_model.apply({"params": pretrained_model_params}, tokens, attention_mask, type_ids, None, None, None, None, deterministic=True)
>>> masked_lm_logits[:, :3, :3]
"""
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 32000
expected_shape = torch.Size((1, 6, vocab_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[-1.7819, -7.7384, -7.5002], [-3.4746, -8.5943, -7.7762], [-3.2052, -9.0771, -8.3468]]],
device=torch_device,
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
@require_tokenizers
def test_inference_long_sentence(self):
model = FNetForMaskedLM.from_pretrained("google/fnet-base")
model.to(torch_device)
tokenizer = FNetTokenizerFast.from_pretrained("google/fnet-base")
inputs = tokenizer(
"the man worked as a [MASK].",
"this is his [MASK].",
return_tensors="pt",
padding="max_length",
max_length=512,
)
inputs = {k: v.to(torch_device) for k, v in inputs.items()}
logits = model(**inputs).logits
predictions_mask_1 = tokenizer.decode(logits[0, 6].topk(5).indices)
predictions_mask_2 = tokenizer.decode(logits[0, 12].topk(5).indices)
self.assertEqual(predictions_mask_1.split(" "), ["man", "child", "teacher", "woman", "model"])
self.assertEqual(predictions_mask_2.split(" "), ["work", "wife", "job", "story", "name"])
@slow
def test_inference_for_next_sentence_prediction(self):
model = FNetForNextSentencePrediction.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 2))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[-0.2234, -0.0226]], device=torch_device)
self.assertTrue(torch.allclose(output, expected_slice, atol=1e-4))
@slow
def test_inference_model(self):
model = FNetModel.from_pretrained("google/fnet-base")
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 6, model.config.hidden_size))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor(
[[[4.1541, -0.1051, -0.1667], [-0.9144, 0.2939, -0.0086], [-0.8472, -0.7281, 0.0256]]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
| 24,325 | 40.370748 | 180 | py |
transformers | transformers-main/tests/models/segformer/test_modeling_tf_segformer.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the TensorFlow SegFormer model. """
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import SegformerConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel
from transformers.models.segformer.modeling_tf_segformer import TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class TFSegformerConfigTester(ConfigTester):
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, "hidden_sizes"))
self.parent.assertTrue(hasattr(config, "num_attention_heads"))
self.parent.assertTrue(hasattr(config, "num_encoder_blocks"))
class TFSegformerModelTester:
def __init__(
self,
parent,
batch_size=13,
image_size=64,
num_channels=3,
num_encoder_blocks=4,
depths=[2, 2, 2, 2],
sr_ratios=[8, 4, 2, 1],
hidden_sizes=[16, 32, 64, 128],
downsampling_rates=[1, 4, 8, 16],
num_attention_heads=[1, 2, 4, 8],
is_training=True,
use_labels=True,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
num_labels=3,
scope=None,
):
self.parent = parent
self.batch_size = batch_size
self.image_size = image_size
self.num_channels = num_channels
self.num_encoder_blocks = num_encoder_blocks
self.sr_ratios = sr_ratios
self.depths = depths
self.hidden_sizes = hidden_sizes
self.downsampling_rates = downsampling_rates
self.num_attention_heads = num_attention_heads
self.is_training = is_training
self.use_labels = use_labels
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
labels = None
if self.use_labels:
labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels)
config = self.get_config()
return config, pixel_values, labels
def get_config(self):
return SegformerConfig(
image_size=self.image_size,
num_channels=self.num_channels,
num_encoder_blocks=self.num_encoder_blocks,
depths=self.depths,
hidden_sizes=self.hidden_sizes,
num_attention_heads=self.num_attention_heads,
hidden_act=self.hidden_act,
hidden_dropout_prob=self.hidden_dropout_prob,
attention_probs_dropout_prob=self.attention_probs_dropout_prob,
initializer_range=self.initializer_range,
num_labels=self.num_labels,
)
def create_and_check_model(self, config, pixel_values, labels):
model = TFSegformerModel(config=config)
result = model(pixel_values, training=False)
expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)
)
def create_and_check_for_image_segmentation(self, config, pixel_values, labels):
config.num_labels = self.num_labels
model = TFSegformerForSemanticSegmentation(config)
result = model(pixel_values, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
)
result = model(pixel_values, labels=labels, training=False)
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, labels = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
def prepare_config_and_inputs_for_keras_fit(self, for_segmentation: bool = False):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values, seg_labels = config_and_inputs
if for_segmentation:
inputs_dict = {"pixel_values": pixel_values, "labels": seg_labels}
else:
inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))}
return config, inputs_dict
@require_tf
class TFSegformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = (
(TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation)
if is_tf_available()
else ()
)
pipeline_model_mapping = (
{"feature-extraction": TFSegformerModel, "image-classification": TFSegformerForImageClassification}
if is_tf_available()
else {}
)
test_head_masking = False
test_onnx = False
test_pruning = False
test_resize_embeddings = False
def setUp(self):
self.model_tester = TFSegformerModelTester(self)
self.config_tester = TFSegformerConfigTester(self, config_class=SegformerConfig, has_text_modality=False)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
@unittest.skip("SegFormer does not use inputs_embeds")
def test_inputs_embeds(self):
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods")
def test_model_common_attributes(self):
pass
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
expected_num_attentions = sum(self.model_tester.depths)
self.assertEqual(len(attentions), expected_num_attentions)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
# verify the last attentions (last block, last layer)
expected_seq_len = (self.model_tester.image_size // 32) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:]),
[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],
)
out_len = len(outputs)
# Check attention is always last and order is fine
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
self.assertEqual(out_len + 1, len(outputs))
self_attentions = outputs.attentions
self.assertEqual(len(self_attentions), expected_num_attentions)
# verify the first attentions (first block, first layer)
expected_seq_len = (self.model_tester.image_size // 4) ** 2
expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],
)
def test_hidden_states_output(self):
def check_hidden_states_output(inputs_dict, config, model_class):
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
hidden_states = outputs.hidden_states
expected_num_layers = self.model_tester.num_encoder_blocks
self.assertEqual(len(hidden_states), expected_num_layers)
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:]),
[
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
],
)
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
inputs_dict["output_hidden_states"] = True
check_hidden_states_output(inputs_dict, config, model_class)
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
config.output_hidden_states = True
check_hidden_states_output(inputs_dict, config, model_class)
def test_model_outputs_equivalence(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
def recursive_check(tuple_object, dict_object):
if isinstance(tuple_object, (List, Tuple)):
for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
recursive_check(tuple_iterable_value, dict_iterable_value)
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(tuple_object, dict_object)),
msg=(
"Tuple and dict output are not equal. Difference:"
f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}"
),
)
recursive_check(tuple_output, dict_output)
for model_class in self.all_model_classes:
model = model_class(config)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs)
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
if self.has_attentions:
tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
dict_inputs = self._prepare_for_class(inputs_dict, model_class)
check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})
# todo: incorporate label support for semantic segmentation in `test_modeling_tf_common.py`.
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
def test_dataset_conversion(self):
super().test_dataset_conversion()
def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1):
self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol))
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
reason="TF does not support backprop for grouped convolutions on CPU.",
)
@slow
def test_keras_fit(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Since `TFSegformerModel` cannot operate with the default `fit()` method.
if model_class.__name__ != "TFSegformerModel":
model = model_class(config)
if getattr(model, "hf_compute_loss", None):
super().test_keras_fit()
def test_loss_computation(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def apply(model):
for_segmentation = True if model_class.__name__ == "TFSegformerForSemanticSegmentation" else False
# The number of elements in the loss should be the same as the number of elements in the label
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit(
for_segmentation=for_segmentation
)
added_label = prepared_for_class[sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]]
loss_size = tf.size(added_label)
# Test that model correctly compute the loss with kwargs
possible_input_names = {"input_ids", "pixel_values", "input_features"}
input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
model_input = prepared_for_class.pop(input_name)
loss = model(model_input, **prepared_for_class)[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
# Semantic segmentation loss is computed similarly as
# https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210.
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
# Test that model correctly compute the loss with a dict
_, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit(
for_segmentation=for_segmentation
)
loss = model(**prepared_for_class)[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
# Test that model correctly compute the loss with a tuple
label_keys = prepared_for_class.keys() - inputs_dict.keys()
signature = inspect.signature(model.call).parameters
signature_names = list(signature.keys())
# Create a dictionary holding the location of the tensors in the tuple
tuple_index_mapping = {0: input_name}
for label_key in label_keys:
label_key_index = signature_names.index(label_key)
tuple_index_mapping[label_key_index] = label_key
sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
# Initialize a list with their default values, update the values and convert to a tuple
list_input = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default)
for index, value in sorted_tuple_index_mapping:
list_input[index] = prepared_for_class[value]
tuple_input = tuple(list_input)
# Send to model
loss = model(tuple_input[:-1])[0]
if model_class.__name__ == "TFSegformerForSemanticSegmentation":
self.assertEqual(loss.shape, (1,))
else:
self.assertEqual(loss.shape, [loss_size])
for model_class in self.all_model_classes:
# Since `TFSegformerModel` won't have labels against which we
# could compute loss.
if model_class.__name__ != "TFSegformerModel":
model = model_class(config)
apply(model)
def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None):
# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes)
@slow
def test_model_from_pretrained(self):
for model_name in TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFSegformerModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
def prepare_img():
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
return image
@require_tf
class TFSegformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_image_segmentation_ade(self):
# only resize + normalize
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
image = prepare_img()
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)
expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
]
)
tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4)
@slow
def test_inference_image_segmentation_city(self):
# only resize + normalize
image_processor = SegformerImageProcessor(
image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
)
image = prepare_img()
encoded_inputs = image_processor(images=image, return_tensors="tf")
pixel_values = encoded_inputs.pixel_values
outputs = model(pixel_values, training=False)
expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128))
self.assertEqual(outputs.logits.shape, expected_shape)
expected_slice = tf.constant(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
]
)
tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1)
| 22,154 | 42.698225 | 117 | py |
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