910 lines
34 KiB
Python
910 lines
34 KiB
Python
# 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 Marian model."""
|
|
|
|
import tempfile
|
|
import unittest
|
|
|
|
from huggingface_hub.hf_api import list_models
|
|
|
|
from transformers import MarianConfig, is_torch_available
|
|
from transformers.testing_utils import (
|
|
require_sentencepiece,
|
|
require_tokenizers,
|
|
require_torch,
|
|
require_torch_fp16,
|
|
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 (
|
|
AutoConfig,
|
|
AutoModelWithLMHead,
|
|
AutoTokenizer,
|
|
MarianModel,
|
|
MarianMTModel,
|
|
TranslationPipeline,
|
|
)
|
|
from transformers.models.marian.convert_marian_to_pytorch import (
|
|
ORG_NAME,
|
|
convert_hf_name_to_opus_name,
|
|
convert_opus_name_to_hf_name,
|
|
)
|
|
from transformers.models.marian.modeling_marian import (
|
|
MarianDecoder,
|
|
MarianEncoder,
|
|
MarianForCausalLM,
|
|
shift_tokens_right,
|
|
)
|
|
|
|
|
|
def prepare_marian_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 MarianModelTester:
|
|
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,
|
|
decoder_start_token_id=3,
|
|
):
|
|
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.decoder_start_token_id = decoder_start_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).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_marian_inputs_dict(config, input_ids, decoder_input_ids)
|
|
return config, inputs_dict
|
|
|
|
def get_config(self):
|
|
return MarianConfig(
|
|
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,
|
|
decoder_start_token_id=self.decoder_start_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 = MarianModel(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 = MarianModel(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 = MarianEncoder.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 = MarianDecoder.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 MarianModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (MarianModel, MarianMTModel) if is_torch_available() else ()
|
|
all_generative_model_classes = (MarianMTModel,) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"conversational": MarianMTModel,
|
|
"feature-extraction": MarianModel,
|
|
"summarization": MarianMTModel,
|
|
"text-generation": MarianForCausalLM,
|
|
"text2text-generation": MarianMTModel,
|
|
"translation": MarianMTModel,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
is_encoder_decoder = True
|
|
fx_compatible = True
|
|
test_pruning = False
|
|
test_missing_keys = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = MarianModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=MarianConfig)
|
|
|
|
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 = MarianMTModel(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 test_share_encoder_decoder_embeddings(self):
|
|
config, input_dict = self.model_tester.prepare_config_and_inputs()
|
|
|
|
# check if embeddings are shared by default
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIs(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens)
|
|
self.assertIs(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight)
|
|
|
|
# check if embeddings are not shared when config.share_encoder_decoder_embeddings = False
|
|
config.share_encoder_decoder_embeddings = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
self.assertIsNot(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens)
|
|
self.assertIsNot(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight)
|
|
|
|
# check if a model with shared embeddings can be saved and loaded with share_encoder_decoder_embeddings = False
|
|
config, _ = 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)
|
|
model = model_class.from_pretrained(tmpdirname, share_encoder_decoder_embeddings=False)
|
|
self.assertIsNot(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens)
|
|
self.assertIsNot(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight)
|
|
|
|
def test_resize_decoder_token_embeddings(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs()
|
|
|
|
# check if resize_decoder_token_embeddings raises an error when embeddings are shared
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
with self.assertRaises(ValueError):
|
|
model.resize_decoder_token_embeddings(config.vocab_size + 1)
|
|
|
|
# check if decoder embeddings are resized when config.share_encoder_decoder_embeddings = False
|
|
config.share_encoder_decoder_embeddings = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
model.resize_decoder_token_embeddings(config.vocab_size + 1)
|
|
self.assertEqual(model.get_decoder().embed_tokens.weight.shape, (config.vocab_size + 1, config.d_model))
|
|
|
|
# check if lm_head is also resized
|
|
config, _ = self.model_tester.prepare_config_and_inputs()
|
|
config.share_encoder_decoder_embeddings = False
|
|
model = MarianMTModel(config)
|
|
model.resize_decoder_token_embeddings(config.vocab_size + 1)
|
|
self.assertEqual(model.lm_head.weight.shape, (config.vocab_size + 1, config.d_model))
|
|
|
|
def test_tie_word_embeddings_decoder(self):
|
|
pass
|
|
|
|
@unittest.skip("Skipping for now, to fix @ArthurZ or @ydshieh")
|
|
def test_pipeline_conversational(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage(self):
|
|
pass
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_checkpoints(self):
|
|
pass
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_no_safetensors(self):
|
|
pass
|
|
|
|
|
|
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)
|
|
|
|
|
|
class ModelManagementTests(unittest.TestCase):
|
|
@slow
|
|
@require_torch
|
|
def test_model_names(self):
|
|
model_list = list_models()
|
|
model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)]
|
|
bad_model_ids = [mid for mid in model_ids if "+" in model_ids]
|
|
self.assertListEqual([], bad_model_ids)
|
|
self.assertGreater(len(model_ids), 500)
|
|
|
|
|
|
@require_torch
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class MarianIntegrationTest(unittest.TestCase):
|
|
src = "en"
|
|
tgt = "de"
|
|
src_text = [
|
|
"I am a small frog.",
|
|
"Now I can forget the 100 words of german that I know.",
|
|
"Tom asked his teacher for advice.",
|
|
"That's how I would do it.",
|
|
"Tom really admired Mary's courage.",
|
|
"Turn around and close your eyes.",
|
|
]
|
|
expected_text = [
|
|
"Ich bin ein kleiner Frosch.",
|
|
"Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
|
|
"Tom bat seinen Lehrer um Rat.",
|
|
"So würde ich das machen.",
|
|
"Tom bewunderte Marias Mut wirklich.",
|
|
"Drehen Sie sich um und schließen Sie die Augen.",
|
|
]
|
|
# ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
|
|
|
|
@classmethod
|
|
def setUpClass(cls) -> None:
|
|
cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
|
|
return cls
|
|
|
|
@cached_property
|
|
def tokenizer(self):
|
|
return AutoTokenizer.from_pretrained(self.model_name)
|
|
|
|
@property
|
|
def eos_token_id(self) -> int:
|
|
return self.tokenizer.eos_token_id
|
|
|
|
@cached_property
|
|
def model(self):
|
|
model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device)
|
|
c = model.config
|
|
self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
|
|
self.assertEqual(c.max_length, 512)
|
|
self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
|
|
|
|
if torch_device == "cuda":
|
|
return model.half()
|
|
else:
|
|
return model
|
|
|
|
def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
|
|
generated_words = self.translate_src_text(**tokenizer_kwargs)
|
|
self.assertListEqual(self.expected_text, generated_words)
|
|
|
|
def translate_src_text(self, **tokenizer_kwargs):
|
|
model_inputs = self.tokenizer(self.src_text, padding=True, return_tensors="pt", **tokenizer_kwargs).to(
|
|
torch_device
|
|
)
|
|
self.assertEqual(self.model.device, model_inputs.input_ids.device)
|
|
generated_ids = self.model.generate(
|
|
model_inputs.input_ids,
|
|
attention_mask=model_inputs.attention_mask,
|
|
num_beams=2,
|
|
max_length=128,
|
|
renormalize_logits=True, # Marian should always renormalize its logits. See #25459
|
|
)
|
|
generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
|
|
return generated_words
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_EN_DE_More(MarianIntegrationTest):
|
|
@slow
|
|
def test_forward(self):
|
|
src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
|
|
expected_ids = [38, 121, 14, 697, 38848, 0]
|
|
|
|
model_inputs = self.tokenizer(src, text_target=tgt, return_tensors="pt").to(torch_device)
|
|
|
|
self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist())
|
|
|
|
desired_keys = {
|
|
"input_ids",
|
|
"attention_mask",
|
|
"labels",
|
|
}
|
|
self.assertSetEqual(desired_keys, set(model_inputs.keys()))
|
|
model_inputs["decoder_input_ids"] = shift_tokens_right(
|
|
model_inputs.labels, self.tokenizer.pad_token_id, self.model.config.decoder_start_token_id
|
|
)
|
|
model_inputs["return_dict"] = True
|
|
model_inputs["use_cache"] = False
|
|
with torch.no_grad():
|
|
outputs = self.model(**model_inputs)
|
|
max_indices = outputs.logits.argmax(-1)
|
|
self.tokenizer.batch_decode(max_indices)
|
|
|
|
def test_unk_support(self):
|
|
t = self.tokenizer
|
|
ids = t(["||"], return_tensors="pt").to(torch_device).input_ids[0].tolist()
|
|
expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
|
|
self.assertEqual(expected, ids)
|
|
|
|
def test_pad_not_split(self):
|
|
input_ids_w_pad = self.tokenizer(["I am a small frog <pad>"], return_tensors="pt").input_ids[0].tolist()
|
|
expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
|
|
self.assertListEqual(expected_w_pad, input_ids_w_pad)
|
|
|
|
@slow
|
|
def test_batch_generation_en_de(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
def test_auto_config(self):
|
|
config = AutoConfig.from_pretrained(self.model_name)
|
|
self.assertIsInstance(config, MarianConfig)
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_EN_FR(MarianIntegrationTest):
|
|
src = "en"
|
|
tgt = "fr"
|
|
src_text = [
|
|
"I am a small frog.",
|
|
"Now I can forget the 100 words of german that I know.",
|
|
]
|
|
expected_text = [
|
|
"Je suis une petite grenouille.",
|
|
"Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
|
|
]
|
|
|
|
@slow
|
|
def test_batch_generation_en_fr(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_FR_EN(MarianIntegrationTest):
|
|
src = "fr"
|
|
tgt = "en"
|
|
src_text = [
|
|
"Donnez moi le micro.",
|
|
"Tom et Mary étaient assis à une table.", # Accents
|
|
]
|
|
expected_text = [
|
|
"Give me the microphone.",
|
|
"Tom and Mary were sitting at a table.",
|
|
]
|
|
|
|
@slow
|
|
def test_batch_generation_fr_en(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_RU_FR(MarianIntegrationTest):
|
|
src = "ru"
|
|
tgt = "fr"
|
|
src_text = ["Он показал мне рукопись своей новой пьесы."]
|
|
expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."]
|
|
|
|
@slow
|
|
def test_batch_generation_ru_fr(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_MT_EN(MarianIntegrationTest):
|
|
"""Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten"""
|
|
|
|
src = "mt"
|
|
tgt = "en"
|
|
src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
|
|
expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
|
|
|
|
@slow
|
|
def test_batch_generation_mt_en(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_en_zh(MarianIntegrationTest):
|
|
src = "en"
|
|
tgt = "zh"
|
|
src_text = ["My name is Wolfgang and I live in Berlin"]
|
|
expected_text = ["我叫沃尔夫冈 我住在柏林"]
|
|
|
|
@slow
|
|
def test_batch_generation_eng_zho(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_en_ROMANCE(MarianIntegrationTest):
|
|
"""Multilingual on target side."""
|
|
|
|
src = "en"
|
|
tgt = "ROMANCE"
|
|
src_text = [
|
|
">>fr<< Don't spend so much time watching TV.",
|
|
">>pt<< Your message has been sent.",
|
|
">>es<< He's two years older than me.",
|
|
]
|
|
expected_text = [
|
|
"Ne passez pas autant de temps à regarder la télé.",
|
|
"A sua mensagem foi enviada.",
|
|
"Es dos años más viejo que yo.",
|
|
]
|
|
|
|
@slow
|
|
def test_batch_generation_en_ROMANCE_multi(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
@slow
|
|
@require_torch
|
|
def test_pipeline(self):
|
|
pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=torch_device)
|
|
output = pipeline(self.src_text)
|
|
self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
|
|
|
|
|
|
@require_sentencepiece
|
|
@require_tokenizers
|
|
class TestMarian_FI_EN_V2(MarianIntegrationTest):
|
|
src = "fi"
|
|
tgt = "en"
|
|
src_text = [
|
|
"minä tykkään kirjojen lukemisesta",
|
|
"Pidän jalkapallon katsomisesta",
|
|
]
|
|
expected_text = ["I like to read books", "I like watching football"]
|
|
|
|
@classmethod
|
|
def setUpClass(cls) -> None:
|
|
cls.model_name = "hf-internal-testing/test-opus-tatoeba-fi-en-v2"
|
|
return cls
|
|
|
|
@slow
|
|
def test_batch_generation_fi_en(self):
|
|
self._assert_generated_batch_equal_expected()
|
|
|
|
|
|
@require_torch
|
|
class TestConversionUtils(unittest.TestCase):
|
|
def test_renaming_multilingual(self):
|
|
old_names = [
|
|
"opus-mt-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
|
|
"opus-mt-cmn+cn-fi", # no group
|
|
"opus-mt-en-de", # standard name
|
|
"opus-mt-en-de", # standard name
|
|
]
|
|
expected = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
|
|
self.assertListEqual(expected, [convert_opus_name_to_hf_name(x) for x in old_names])
|
|
|
|
def test_undoing_renaming(self):
|
|
hf_names = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"]
|
|
converted_opus_names = [convert_hf_name_to_opus_name(x) for x in hf_names]
|
|
expected_opus_names = [
|
|
"cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi",
|
|
"cmn+cn-fi",
|
|
"en-de", # standard name
|
|
"en-de",
|
|
]
|
|
self.assertListEqual(expected_opus_names, converted_opus_names)
|
|
|
|
|
|
class MarianStandaloneDecoderModelTester:
|
|
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=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 = MarianConfig(
|
|
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 = MarianDecoder(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 = MarianDecoder(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 MarianStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (MarianDecoder, MarianForCausalLM) if is_torch_available() else ()
|
|
all_generative_model_classes = (MarianForCausalLM,) if is_torch_available() else ()
|
|
test_pruning = False
|
|
is_encoder_decoder = False
|
|
|
|
def setUp(
|
|
self,
|
|
):
|
|
self.model_tester = MarianStandaloneDecoderModelTester(self, is_training=False)
|
|
self.config_tester = ConfigTester(self, config_class=MarianConfig)
|
|
|
|
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
|