SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Generation tests: update imagegpt input name, remove unused functions (#33663)

Browse Source
This commit is contained in:
Joao Gante
2024-09-24 16:40:48 +01:00
committed by GitHub
parent 6f7d750b73
commit a7734238ff
18 changed files with 23 additions and 656 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
src/transformers/models/imagegpt/modeling_imagegpt.py
View File

@@ -690,8 +690,7 @@ class ImageGPTModel(ImageGPTPreTrainedModel):
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)
@@ -1004,8 +1003,7 @@ class ImageGPTForCausalImageModeling(ImageGPTPreTrainedModel, GenerationMixin):
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)
@@ -1137,8 +1135,7 @@ class ImageGPTForImageClassification(ImageGPTPreTrainedModel):
if "pixel_values" in kwargs:
warnings.warn(
"The `pixel_values` argument is deprecated and will be removed in a future version, use `input_ids`"
" instead.",
"The `pixel_values` argument is deprecated and will be removed in v4.47, use `input_ids` instead.",
FutureWarning,
)

20
tests/generation/test_utils.py
View File

@@ -190,26 +190,6 @@ class GenerationTesterMixin:
}
return beam_kwargs
@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,
attention_mask=attention_mask,
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
)
generation_config = copy.deepcopy(model.generation_config)
model._prepare_special_tokens(generation_config)
input_ids = torch.zeros_like(input_ids[:, :1]) + generation_config.decoder_start_token_id
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def _greedy_generate(
self,
model,

31
tests/models/codegen/test_modeling_codegen.py
View File

@@ -23,7 +23,7 @@ from transformers.testing_utils import backend_manual_seed, is_flaky, require_to
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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -150,35 +150,6 @@ class CodeGenModelTester:
rotary_dim=self.rotary_dim,
)
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_codegen_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = CodeGenModel(config=config)
model.to(torch_device)

19
tests/models/falcon_mamba/test_modeling_falcon_mamba.py
View File

@@ -150,25 +150,6 @@ class FalconMambaModelTester:
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
return (
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_falcon_mamba_model(self, config, input_ids, *args):
config.output_hidden_states = True
model = FalconMambaModel(config=config)

31
tests/models/gpt_bigcode/test_modeling_gpt_bigcode.py
View File

@@ -22,7 +22,7 @@ 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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -178,35 +178,6 @@ class GPTBigCodeModelTester:
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_gpt_bigcode_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTBigCodeModel(config=config)
model.to(torch_device)

31
tests/models/gpt_neo/test_modeling_gpt_neo.py
View File

@@ -22,7 +22,7 @@ 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, random_attention_mask
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -157,35 +157,6 @@ class GPTNeoModelTester:
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_gpt_neo_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
model = GPTNeoModel(config=config)
model.to(torch_device)

31
tests/models/gptj/test_modeling_gptj.py
View File

@@ -32,7 +32,7 @@ from transformers.testing_utils import (
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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -173,35 +173,6 @@ class GPTJModelTester:
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)

287
tests/models/imagegpt/test_modeling_imagegpt.py
View File

@@ -14,10 +14,7 @@
# limitations under the License.
import copy
import inspect
import os
import tempfile
import unittest
from transformers import ImageGPTConfig
@@ -26,13 +23,7 @@ from transformers.utils import cached_property, is_torch_available, is_vision_av
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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -108,7 +99,7 @@ class ImageGPTModelTester:
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_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size - 1)
input_mask = None
if self.use_input_mask:
@@ -140,7 +131,7 @@ class ImageGPTModelTester:
return (
config,
pixel_values,
input_ids,
input_mask,
head_mask,
token_type_ids,
@@ -177,66 +168,37 @@ class ImageGPTModelTester:
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):
def create_and_check_imagegpt_model(self, config, input_ids, 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)
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_lm_head_model(self, config, pixel_values, input_mask, head_mask, token_type_ids, *args):
def create_and_check_lm_head_model(self, config, input_ids, 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)
result = model(input_ids, 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
self, config, input_ids, 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)
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 prepare_config_and_inputs_for_common(self):
@@ -244,7 +206,7 @@ class ImageGPTModelTester:
(
config,
pixel_values,
input_ids,
input_mask,
head_mask,
token_type_ids,
@@ -255,7 +217,7 @@ class ImageGPTModelTester:
) = config_and_inputs
inputs_dict = {
"pixel_values": pixel_values,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"head_mask": head_mask,
}
@@ -275,7 +237,6 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
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):
@@ -351,228 +312,6 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
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:
self.skipTest(reason="test_resize_embeddings is set to False")
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:
self.skipTest(reason="test_resize_embeddings is set to False")
original_config.tie_word_embeddings = False
# if model cannot untied embeddings -> leave test
if original_config.tie_word_embeddings:
self.skipTest(reason="tie_word_embeddings is set to 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 = 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]
# override because ImageGPT main input name is `pixel_values`
# NOTE: in latest transformers this is deprecated, `input_ids` should be used. TODO
def test_inputs_embeds_matches_input_ids(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():
out_ids = model(**inputs)[0]
pixel_values = inputs["pixel_values"]
del inputs["pixel_values"]
wte = model.get_input_embeddings()
inputs["inputs_embeds"] = wte(pixel_values)
with torch.no_grad():
out_embeds = model(**inputs)[0]
self.assertTrue(torch.allclose(out_embeds, out_ids))
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
self.skipTest(reason="test_torchscript is set to False")
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(reason="The model doesn't support left padding") # and it's not used enough to be worth fixing :)
def test_left_padding_compatibility(self):
pass

19
tests/models/mamba/test_modeling_mamba.py
View File

@@ -143,25 +143,6 @@ class MambaModelTester:
config.vocab_size = 300
return config
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
return (
config,
input_ids,
attention_mask,
sequence_labels,
token_labels,
choice_labels,
)
def create_and_check_mamba_model(self, config, input_ids, *args):
config.output_hidden_states = True
model = MambaModel(config=config)

31
tests/models/rwkv/test_modeling_rwkv.py
View File

@@ -22,7 +22,7 @@ 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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -161,35 +161,6 @@ class RwkvModelTester:
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_rwkv_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args):
config.output_hidden_states = True
model = RwkvModel(config=config)

23
tests/models/seamless_m4t/test_modeling_seamless_m4t.py
View File

@@ -399,29 +399,6 @@ class SeamlessM4TModelWithSpeechInputTest(ModelTesterMixin, unittest.TestCase):
return config, input_ids.float(), attention_mask, max_length
@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,
attention_mask=attention_mask,
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
)
generation_config = copy.deepcopy(model.generation_config)
model._prepare_special_tokens(generation_config)
input_ids = (
torch.zeros(input_ids.shape[:2], dtype=torch.int64, layout=input_ids.layout, device=input_ids.device)
+ generation_config.decoder_start_token_id
)
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

23
tests/models/seamless_m4t_v2/test_modeling_seamless_m4t_v2.py
View File

@@ -415,29 +415,6 @@ class SeamlessM4Tv2ModelWithSpeechInputTest(ModelTesterMixin, unittest.TestCase)
return config, input_ids.float(), attention_mask, max_length
@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,
attention_mask=attention_mask,
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
)
generation_config = copy.deepcopy(model.generation_config)
model._prepare_special_tokens(generation_config)
input_ids = (
torch.zeros(input_ids.shape[:2], dtype=torch.int64, layout=input_ids.layout, device=input_ids.device)
+ generation_config.decoder_start_token_id
)
attention_mask = None
return encoder_outputs, input_ids, attention_mask
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

21
tests/models/speech_to_text/test_modeling_speech_to_text.py
View File

@@ -632,27 +632,6 @@ class Speech2TextModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTest
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,
attention_mask=attention_mask,
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]
generation_config = copy.deepcopy(model.generation_config)
model._prepare_special_tokens(generation_config)
input_ids = torch.zeros_like(input_ids[:, :1]) + generation_config.decoder_start_token_id
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, seq_length = input_ids.shape[:2]
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)

18
tests/models/speech_to_text/test_modeling_tf_speech_to_text.py
View File

@@ -416,24 +416,6 @@ class TFSpeech2TextModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.T
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,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0)
input_ids = input_ids[:, :, 0]
input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id()
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, seq_length = input_ids.shape[:2]
subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length)

20
tests/models/whisper/test_modeling_tf_whisper.py
View File

@@ -519,26 +519,6 @@ class TFWhisperModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestC
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 = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + tf.convert_to_tensor(
[model._get_decoder_start_token_id()]
)
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)

27
tests/models/whisper/test_modeling_whisper.py
View File

@@ -868,26 +868,6 @@ class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
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
)
generation_config = copy.deepcopy(model.generation_config)
model._prepare_special_tokens(generation_config)
input_ids = input_ids[:, :, 0]
input_ids = torch.zeros_like(input_ids[:, :1], dtype=torch.long) + generation_config.decoder_start_token_id
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)
@@ -3894,13 +3874,6 @@ class WhisperStandaloneDecoderModelTester:
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config, input_features = self.prepare_config_and_inputs()
input_ids = input_features["input_ids"]
encoder_hidden_states = floats_tensor([self.batch_size, self.decoder_seq_length, self.hidden_size])
return (config, input_ids, encoder_hidden_states)
def create_and_check_decoder_model_past(self, config, input_ids):
config.use_cache = True
model = WhisperDecoder(config=config).to(torch_device).eval()

16
tests/models/xglm/test_modeling_flax_xglm.py
View File

@@ -22,7 +22,7 @@ from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
@@ -116,20 +116,6 @@ class FlaxXGLMModelTester:
inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
def prepare_config_and_inputs_for_decoder(self):
config, input_ids, attention_mask = 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,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
)
def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
max_decoder_length = 20
model = model_class_name(config)

22
tests/models/xglm/test_modeling_xglm.py
View File

@@ -29,7 +29,7 @@ from transformers.testing_utils import (
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_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -125,26 +125,6 @@ class XGLMModelTester:
gradient_checkpointing=gradient_checkpointing,
)
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
input_mask,
head_mask,
) = 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,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_xglm_model(self, config, input_ids, input_mask, head_mask, *args):
model = XGLMModel(config=config)
model.to(torch_device)