init
This commit is contained in:
@@ -0,0 +1,570 @@
|
||||
# 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 BlenderbotSmall model."""
|
||||
|
||||
import tempfile
|
||||
import unittest
|
||||
from functools import cached_property
|
||||
|
||||
from transformers import BlenderbotSmallConfig, is_torch_available
|
||||
from transformers.testing_utils import (
|
||||
require_torch,
|
||||
require_torch_fp16,
|
||||
slow,
|
||||
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 import BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallTokenizer
|
||||
from transformers.models.blenderbot_small.modeling_blenderbot_small import (
|
||||
BlenderbotSmallDecoder,
|
||||
BlenderbotSmallEncoder,
|
||||
BlenderbotSmallForCausalLM,
|
||||
)
|
||||
|
||||
|
||||
def prepare_blenderbot_small_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 BlenderbotSmallModelTester:
|
||||
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=50,
|
||||
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).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_blenderbot_small_inputs_dict(config, input_ids, decoder_input_ids)
|
||||
return config, inputs_dict
|
||||
|
||||
def get_config(self):
|
||||
return BlenderbotSmallConfig(
|
||||
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 = BlenderbotSmallModel(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 = BlenderbotSmallModel(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 = BlenderbotSmallEncoder.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 = BlenderbotSmallDecoder.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 BlenderbotSmallModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (BlenderbotSmallModel, BlenderbotSmallForConditionalGeneration) if is_torch_available() else ()
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": BlenderbotSmallModel,
|
||||
"summarization": BlenderbotSmallForConditionalGeneration,
|
||||
"text-generation": BlenderbotSmallForCausalLM,
|
||||
"text2text-generation": BlenderbotSmallForConditionalGeneration,
|
||||
"translation": BlenderbotSmallForConditionalGeneration,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
is_encoder_decoder = True
|
||||
fx_compatible = True
|
||||
test_pruning = False
|
||||
test_missing_keys = False
|
||||
|
||||
# TODO: Fix the failed tests when this model gets more usage
|
||||
def is_pipeline_test_to_skip(
|
||||
self,
|
||||
pipeline_test_case_name,
|
||||
config_class,
|
||||
model_architecture,
|
||||
tokenizer_name,
|
||||
image_processor_name,
|
||||
feature_extractor_name,
|
||||
processor_name,
|
||||
):
|
||||
return pipeline_test_case_name == "TextGenerationPipelineTests"
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = BlenderbotSmallModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
|
||||
|
||||
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)
|
||||
|
||||
@require_torch_fp16
|
||||
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 = BlenderbotSmallForConditionalGeneration(config).eval().to(torch_device)
|
||||
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)
|
||||
|
||||
|
||||
@require_torch
|
||||
class Blenderbot90MIntegrationTests(unittest.TestCase):
|
||||
ckpt = "facebook/blenderbot-90M"
|
||||
|
||||
@cached_property
|
||||
def model(self):
|
||||
model = BlenderbotSmallForConditionalGeneration.from_pretrained(self.ckpt).to(torch_device)
|
||||
if torch_device == "cuda":
|
||||
model = model.half()
|
||||
return model
|
||||
|
||||
@cached_property
|
||||
def tokenizer(self):
|
||||
return BlenderbotSmallTokenizer.from_pretrained(self.ckpt)
|
||||
|
||||
@slow
|
||||
def test_90_generation_from_long_input(self):
|
||||
src_text = [
|
||||
"Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel"
|
||||
" like i'm going to throw up.\nand why is that?"
|
||||
]
|
||||
|
||||
model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device)
|
||||
|
||||
assert isinstance(self.tokenizer, BlenderbotSmallTokenizer)
|
||||
generated_ids = self.model.generate(**model_inputs)[0]
|
||||
reply = self.tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
|
||||
|
||||
assert reply in (
|
||||
"i don't know. i just feel like i'm going to throw up. it's not fun.",
|
||||
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
|
||||
)
|
||||
|
||||
@slow
|
||||
def test_90_generation_from_short_input(self):
|
||||
model_inputs = self.tokenizer(["sam"], return_tensors="pt").to(torch_device)
|
||||
|
||||
generated_utterances = self.model.generate(**model_inputs)
|
||||
|
||||
clean_txt = self.tokenizer.decode(
|
||||
generated_utterances[0], skip_special_tokens=True, clean_up_tokenization_spaces=True
|
||||
)
|
||||
assert clean_txt in (
|
||||
"have you ever been to a sam club? it's a great club in the south.",
|
||||
"have you ever heard of sam harris? he's an american singer, songwriter, and actor.",
|
||||
)
|
||||
|
||||
|
||||
class BlenderbotSmallStandaloneDecoderModelTester:
|
||||
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=2,
|
||||
encoder_attention_heads=4,
|
||||
decoder_attention_heads=4,
|
||||
max_position_embeddings=50,
|
||||
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 = BlenderbotSmallConfig(
|
||||
vocab_size=self.vocab_size,
|
||||
d_model=self.d_model,
|
||||
decoder_layers=self.decoder_layers,
|
||||
num_hidden_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 = BlenderbotSmallDecoder(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 = BlenderbotSmallDecoder(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, past_key_values=past_key_values, attention_mask=attn_mask, use_cache=True
|
||||
)["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 BlenderbotSmallStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (BlenderbotSmallDecoder, BlenderbotSmallForCausalLM) if is_torch_available() else ()
|
||||
test_pruning = False
|
||||
is_encoder_decoder = False
|
||||
|
||||
def setUp(
|
||||
self,
|
||||
):
|
||||
self.model_tester = BlenderbotSmallStandaloneDecoderModelTester(self, is_training=False)
|
||||
self.config_tester = ConfigTester(self, config_class=BlenderbotSmallConfig)
|
||||
|
||||
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)
|
||||
|
||||
@unittest.skip(reason="decoder cannot keep gradients")
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
return
|
||||
|
||||
@unittest.skip(reason="Decoder cannot keep gradients")
|
||||
def test_flex_attention_with_grads():
|
||||
return
|
||||
@@ -0,0 +1,90 @@
|
||||
#!/usr/bin/env python3
|
||||
# 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.
|
||||
"""Tests for the Blenderbot small tokenizer."""
|
||||
|
||||
import json
|
||||
import os
|
||||
import unittest
|
||||
|
||||
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
|
||||
VOCAB_FILES_NAMES,
|
||||
BlenderbotSmallTokenizer,
|
||||
)
|
||||
|
||||
from ...test_tokenization_common import TokenizerTesterMixin
|
||||
|
||||
|
||||
class BlenderbotSmallTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
|
||||
from_pretrained_id = "facebook/blenderbot_small-90M"
|
||||
tokenizer_class = BlenderbotSmallTokenizer
|
||||
test_rust_tokenizer = False
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
super().setUpClass()
|
||||
|
||||
vocab = ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
|
||||
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
||||
|
||||
merges = ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
|
||||
cls.special_tokens_map = {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}
|
||||
|
||||
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
|
||||
cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
|
||||
with open(cls.vocab_file, "w", encoding="utf-8") as fp:
|
||||
fp.write(json.dumps(vocab_tokens) + "\n")
|
||||
with open(cls.merges_file, "w", encoding="utf-8") as fp:
|
||||
fp.write("\n".join(merges))
|
||||
|
||||
@classmethod
|
||||
def get_tokenizer(cls, pretrained_name=None, **kwargs):
|
||||
kwargs.update(cls.special_tokens_map)
|
||||
pretrained_name = pretrained_name or cls.tmpdirname
|
||||
return BlenderbotSmallTokenizer.from_pretrained(pretrained_name, **kwargs)
|
||||
|
||||
def get_input_output_texts(self, tokenizer):
|
||||
input_text = "adapt act apte"
|
||||
output_text = "adapt act apte"
|
||||
return input_text, output_text
|
||||
|
||||
def test_full_blenderbot_small_tokenizer(self):
|
||||
tokenizer = BlenderbotSmallTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
|
||||
text = "adapt act apte"
|
||||
bpe_tokens = ["adapt", "act", "ap@@", "te"]
|
||||
tokens = tokenizer.tokenize(text)
|
||||
self.assertListEqual(tokens, bpe_tokens)
|
||||
|
||||
input_tokens = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]
|
||||
|
||||
input_bpe_tokens = [0, 1, 2, 3, 4, 5]
|
||||
self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
|
||||
|
||||
def test_special_tokens_small_tok(self):
|
||||
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
|
||||
assert tok("sam").input_ids == [1384]
|
||||
src_text = "I am a small frog."
|
||||
encoded = tok([src_text], padding=False, truncation=False)["input_ids"]
|
||||
decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
assert src_text != decoded # I wish it did!
|
||||
assert decoded == "i am a small frog ."
|
||||
|
||||
def test_empty_word_small_tok(self):
|
||||
tok = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M")
|
||||
src_text = "I am a small frog ."
|
||||
src_text_dot = "."
|
||||
encoded = tok(src_text)["input_ids"]
|
||||
encoded_dot = tok(src_text_dot)["input_ids"]
|
||||
|
||||
assert encoded[-1] == encoded_dot[0]
|
||||
Reference in New Issue
Block a user