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* first try * remove old template * finish bart * finish mbart * delete unnecessary line * init pegasus * save intermediate * correct pegasus * finish pegasus * remove cookie cutter leftover * add marian * finish blenderbot * replace in file * correctly split blenderbot * delete "old" folder * correct "add statement" * adapt config for tf comp * correct configs for tf * remove ipdb * fix more stuff * fix mbart * push pegasus fix * fix mbart * more fixes * fix research projects code * finish docs for bart, mbart, and marian * delete unnecessary file * correct attn typo * correct configs * remove pegasus for seq class * correct peg docs * correct peg docs * finish configs * further improve docs * add copied from statements to mbart * fix copied from in mbart * add copy statements to marian * add copied from to marian * add pegasus copied from * finish pegasus * finish copied from * Apply suggestions from code review * make style * backward comp blenderbot * apply lysandres and sylvains suggestions * apply suggestions * push last fixes * fix docs * fix tok tests * fix imports code style * fix doc
547 lines
20 KiB
Python
547 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2021, The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the PyTorch Marian model. """
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import tempfile
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import unittest
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from transformers import is_torch_available
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from transformers.file_utils import cached_property
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from transformers.hf_api import HfApi
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_generation_utils import GenerationTesterMixin
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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import torch
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from transformers import (
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AutoConfig,
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AutoModelWithLMHead,
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AutoTokenizer,
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MarianConfig,
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MarianModel,
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MarianMTModel,
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TranslationPipeline,
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)
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from transformers.models.marian.convert_marian_to_pytorch import (
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ORG_NAME,
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convert_hf_name_to_opus_name,
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convert_opus_name_to_hf_name,
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)
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from transformers.models.marian.modeling_marian import MarianDecoder, MarianEncoder, shift_tokens_right
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def prepare_marian_inputs_dict(
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config,
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input_ids,
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decoder_input_ids,
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attention_mask=None,
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decoder_attention_mask=None,
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):
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if attention_mask is None:
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attention_mask = input_ids.ne(config.pad_token_id)
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if decoder_attention_mask is None:
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decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
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return {
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"input_ids": input_ids,
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"decoder_input_ids": decoder_input_ids,
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"attention_mask": attention_mask,
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"decoder_attention_mask": attention_mask,
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}
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@require_torch
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class MarianModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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decoder_start_token_id=3,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.decoder_start_token_id = decoder_start_token_id
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
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3,
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)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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config = MarianConfig(
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vocab_size=self.vocab_size,
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d_model=self.hidden_size,
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encoder_layers=self.num_hidden_layers,
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decoder_layers=self.num_hidden_layers,
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encoder_attention_heads=self.num_attention_heads,
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decoder_attention_heads=self.num_attention_heads,
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encoder_ffn_dim=self.intermediate_size,
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decoder_ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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def prepare_config_and_inputs_for_common(self):
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config, inputs_dict = self.prepare_config_and_inputs()
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return config, inputs_dict
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def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
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model = MarianModel(config=config).get_decoder().to(torch_device).eval()
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input_ids = inputs_dict["input_ids"]
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attention_mask = inputs_dict["attention_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, use_cache=True)
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output, past_key_values = outputs.to_tuple()
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
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next_attn_mask = ids_tensor((self.batch_size, 3), 2)
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# append to next input_ids and
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next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
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next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
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output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
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output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
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"last_hidden_state"
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]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
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def check_encoder_decoder_model_standalone(self, config, inputs_dict):
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model = MarianModel(config=config).to(torch_device).eval()
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outputs = model(**inputs_dict)
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encoder_last_hidden_state = outputs.encoder_last_hidden_state
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last_hidden_state = outputs.last_hidden_state
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with tempfile.TemporaryDirectory() as tmpdirname:
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encoder = model.get_encoder()
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encoder.save_pretrained(tmpdirname)
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encoder = MarianEncoder.from_pretrained(tmpdirname).to(torch_device)
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encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
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0
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]
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self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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decoder = model.get_decoder()
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decoder.save_pretrained(tmpdirname)
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decoder = MarianDecoder.from_pretrained(tmpdirname).to(torch_device)
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last_hidden_state_2 = decoder(
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input_ids=inputs_dict["decoder_input_ids"],
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attention_mask=inputs_dict["decoder_attention_mask"],
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encoder_hidden_states=encoder_last_hidden_state,
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encoder_attention_mask=inputs_dict["attention_mask"],
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)[0]
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self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
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@require_torch
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class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (MarianModel, MarianMTModel) if is_torch_available() else ()
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all_generative_model_classes = (MarianMTModel,) if is_torch_available() else ()
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is_encoder_decoder = True
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test_pruning = False
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test_head_masking = False
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test_missing_keys = False
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def setUp(self):
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self.model_tester = MarianModelTester(self)
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self.config_tester = ConfigTester(self, config_class=MarianConfig)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_save_load_strict(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs()
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for model_class in self.all_model_classes:
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model = model_class(config)
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
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self.assertEqual(info["missing_keys"], [])
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def test_decoder_model_past_with_large_inputs(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
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def test_encoder_decoder_model_standalone(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
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self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
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def test_generate_fp16(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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input_ids = input_dict["input_ids"]
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attention_mask = input_ids.ne(1).to(torch_device)
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model = MarianMTModel(config).eval().to(torch_device)
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if torch_device == "cuda":
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model.half()
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model.generate(input_ids, attention_mask=attention_mask)
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model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
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def assert_tensors_close(a, b, atol=1e-12, prefix=""):
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"""If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""
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if a is None and b is None:
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return True
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try:
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if torch.allclose(a, b, atol=atol):
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return True
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raise
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except Exception:
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pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item()
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if a.numel() > 100:
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msg = f"tensor values are {pct_different:.1%} percent different."
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else:
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msg = f"{a} != {b}"
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if prefix:
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msg = prefix + ": " + msg
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raise AssertionError(msg)
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def _long_tensor(tok_lst):
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return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
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class ModelManagementTests(unittest.TestCase):
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@slow
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@require_torch
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def test_model_names(self):
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model_list = HfApi().model_list()
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model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)]
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bad_model_ids = [mid for mid in model_ids if "+" in model_ids]
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self.assertListEqual([], bad_model_ids)
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self.assertGreater(len(model_ids), 500)
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@require_torch
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@require_sentencepiece
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@require_tokenizers
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class MarianIntegrationTest(unittest.TestCase):
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src = "en"
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tgt = "de"
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src_text = [
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"I am a small frog.",
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"Now I can forget the 100 words of german that I know.",
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"Tom asked his teacher for advice.",
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"That's how I would do it.",
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"Tom really admired Mary's courage.",
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"Turn around and close your eyes.",
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]
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expected_text = [
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"Ich bin ein kleiner Frosch.",
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"Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
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"Tom bat seinen Lehrer um Rat.",
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"So würde ich das machen.",
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"Tom bewunderte Marias Mut wirklich.",
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"Drehen Sie sich um und schließen Sie die Augen.",
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]
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# ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen
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@classmethod
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def setUpClass(cls) -> None:
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cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
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return cls
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@cached_property
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def tokenizer(self):
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return AutoTokenizer.from_pretrained(self.model_name)
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@property
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def eos_token_id(self) -> int:
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return self.tokenizer.eos_token_id
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@cached_property
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def model(self):
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model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device)
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c = model.config
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self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]])
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self.assertEqual(c.max_length, 512)
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self.assertEqual(c.decoder_start_token_id, c.pad_token_id)
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if torch_device == "cuda":
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return model.half()
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else:
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return model
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def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs):
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generated_words = self.translate_src_text(**tokenizer_kwargs)
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self.assertListEqual(self.expected_text, generated_words)
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def translate_src_text(self, **tokenizer_kwargs):
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model_inputs = self.tokenizer.prepare_seq2seq_batch(
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src_texts=self.src_text, return_tensors="pt", **tokenizer_kwargs
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).to(torch_device)
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self.assertEqual(self.model.device, model_inputs.input_ids.device)
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generated_ids = self.model.generate(
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model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128
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)
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generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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return generated_words
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@require_sentencepiece
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@require_tokenizers
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class TestMarian_EN_DE_More(MarianIntegrationTest):
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@slow
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def test_forward(self):
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src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."]
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expected_ids = [38, 121, 14, 697, 38848, 0]
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model_inputs: dict = self.tokenizer.prepare_seq2seq_batch(src, tgt_texts=tgt, return_tensors="pt").to(
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torch_device
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)
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self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist())
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desired_keys = {
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"input_ids",
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"attention_mask",
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"labels",
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}
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self.assertSetEqual(desired_keys, set(model_inputs.keys()))
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model_inputs["decoder_input_ids"] = shift_tokens_right(
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model_inputs.labels, self.tokenizer.pad_token_id, self.model.config.decoder_start_token_id
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)
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model_inputs["return_dict"] = True
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model_inputs["use_cache"] = False
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with torch.no_grad():
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outputs = self.model(**model_inputs)
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max_indices = outputs.logits.argmax(-1)
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self.tokenizer.batch_decode(max_indices)
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def test_unk_support(self):
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t = self.tokenizer
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ids = t.prepare_seq2seq_batch(["||"], return_tensors="pt").to(torch_device).input_ids[0].tolist()
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expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id]
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self.assertEqual(expected, ids)
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def test_pad_not_split(self):
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input_ids_w_pad = (
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self.tokenizer.prepare_seq2seq_batch(["I am a small frog <pad>"], return_tensors="pt")
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.input_ids[0]
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.tolist()
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)
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expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad
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self.assertListEqual(expected_w_pad, input_ids_w_pad)
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@slow
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def test_batch_generation_en_de(self):
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self._assert_generated_batch_equal_expected()
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def test_auto_config(self):
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config = AutoConfig.from_pretrained(self.model_name)
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self.assertIsInstance(config, MarianConfig)
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@require_sentencepiece
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@require_tokenizers
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class TestMarian_EN_FR(MarianIntegrationTest):
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src = "en"
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tgt = "fr"
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src_text = [
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"I am a small frog.",
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"Now I can forget the 100 words of german that I know.",
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]
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expected_text = [
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"Je suis une petite grenouille.",
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"Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
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]
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@slow
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def test_batch_generation_en_fr(self):
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self._assert_generated_batch_equal_expected()
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@require_sentencepiece
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@require_tokenizers
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class TestMarian_FR_EN(MarianIntegrationTest):
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src = "fr"
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tgt = "en"
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src_text = [
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"Donnez moi le micro.",
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"Tom et Mary étaient assis à une table.", # Accents
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]
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expected_text = [
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"Give me the microphone.",
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"Tom and Mary were sitting at a table.",
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]
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@slow
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def test_batch_generation_fr_en(self):
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self._assert_generated_batch_equal_expected()
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@require_sentencepiece
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@require_tokenizers
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class TestMarian_RU_FR(MarianIntegrationTest):
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src = "ru"
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tgt = "fr"
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src_text = ["Он показал мне рукопись своей новой пьесы."]
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expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."]
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@slow
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def test_batch_generation_ru_fr(self):
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self._assert_generated_batch_equal_expected()
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@require_sentencepiece
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@require_tokenizers
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class TestMarian_MT_EN(MarianIntegrationTest):
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"""Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten"""
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src = "mt"
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tgt = "en"
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src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
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expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
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@slow
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def test_batch_generation_mt_en(self):
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self._assert_generated_batch_equal_expected()
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@require_sentencepiece
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@require_tokenizers
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|
class TestMarian_en_zh(MarianIntegrationTest):
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src = "en"
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tgt = "zh"
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src_text = ["My name is Wolfgang and I live in Berlin"]
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expected_text = ["我叫沃尔夫冈 我住在柏林"]
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@slow
|
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def test_batch_generation_eng_zho(self):
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self._assert_generated_batch_equal_expected()
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|
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|
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@require_sentencepiece
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|
@require_tokenizers
|
|
class TestMarian_en_ROMANCE(MarianIntegrationTest):
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"""Multilingual on target side."""
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|
|
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src = "en"
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tgt = "ROMANCE"
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src_text = [
|
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">>fr<< Don't spend so much time watching TV.",
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">>pt<< Your message has been sent.",
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">>es<< He's two years older than me.",
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|
]
|
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expected_text = [
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"Ne passez pas autant de temps à regarder la télé.",
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"A sua mensagem foi enviada.",
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"Es dos años más viejo que yo.",
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]
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|
|
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@slow
|
|
def test_batch_generation_en_ROMANCE_multi(self):
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|
self._assert_generated_batch_equal_expected()
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|
|
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def test_tokenizer_handles_empty(self):
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|
normalized = self.tokenizer.normalize("")
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self.assertIsInstance(normalized, str)
|
|
with self.assertRaises(ValueError):
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self.tokenizer.prepare_seq2seq_batch([""], return_tensors="pt")
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|
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@slow
|
|
def test_pipeline(self):
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|
device = 0 if torch_device == "cuda" else -1
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pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=device)
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output = pipeline(self.src_text)
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self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
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|
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|
|
@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])
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|
|
|
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)
|