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https://github.com/huggingface/transformers.git
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dcec4c4387
* Adding OPTForSeqClassification class * Fix import issues * Add documentation for optforseqclassification * Remove checkout * fix failing tests * fix typo * Fix code formatting * Incorporating the PR feedbacks * Incorporate PR Feedbacks * Fix failing test and add new test for multi label setup * Fix formatting issue * Fix failing tests * Fix formatting issues * Fix failing tests * Fix failing tests * Fix failing tests * Fix failing tests * PR feedback
483 lines
19 KiB
Python
483 lines
19 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 OPT model. """
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import copy
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import tempfile
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import unittest
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import timeout_decorator # noqa
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from transformers import OPTConfig, is_torch_available
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from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
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from ...generation.test_generation_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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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 GPT2Tokenizer, OPTForCausalLM, OPTForSequenceClassification, OPTModel
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def prepare_opt_inputs_dict(
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config,
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input_ids,
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decoder_input_ids=None,
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attention_mask=None,
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decoder_attention_mask=None,
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head_mask=None,
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decoder_head_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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return {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"head_mask": head_mask,
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}
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class OPTModelTester:
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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=5,
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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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embed_dim=16,
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num_labels=3,
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word_embed_proj_dim=16,
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type_sequence_label_size=2,
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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.embed_dim = embed_dim
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self.num_labels = num_labels
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self.type_sequence_label_size = type_sequence_label_size
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self.word_embed_proj_dim = word_embed_proj_dim
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self.is_encoder_decoder = False
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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).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 = self.get_config()
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inputs_dict = prepare_opt_inputs_dict(config, input_ids, decoder_input_ids)
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return config, inputs_dict
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def get_config(self):
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return OPTConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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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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embed_dim=self.embed_dim,
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is_encoder_decoder=False,
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word_embed_proj_dim=self.word_embed_proj_dim,
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)
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def get_pipeline_config(self):
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config = self.get_config()
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config.max_position_embeddings = 100
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return config
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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 = OPTModel(config=config).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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head_mask = inputs_dict["head_mask"]
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# first forward pass
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outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_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-3))
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@require_torch
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class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
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all_model_classes = (OPTModel, OPTForCausalLM, OPTForSequenceClassification) if is_torch_available() else ()
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all_generative_model_classes = (OPTForCausalLM,) if is_torch_available() else ()
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is_encoder_decoder = False
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fx_compatible = True
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test_pruning = False
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test_missing_keys = False
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def setUp(self):
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self.model_tester = OPTModelTester(self)
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self.config_tester = ConfigTester(self, config_class=OPTConfig)
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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_inputs_embeds(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in (OPTModel,):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
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if not self.is_encoder_decoder:
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input_ids = inputs["input_ids"]
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del inputs["input_ids"]
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else:
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encoder_input_ids = inputs["input_ids"]
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decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
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del inputs["input_ids"]
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inputs.pop("decoder_input_ids", None)
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wte = model.get_input_embeddings()
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if not self.is_encoder_decoder:
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inputs["inputs_embeds"] = wte(input_ids)
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else:
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inputs["inputs_embeds"] = wte(encoder_input_ids)
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inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
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with torch.no_grad():
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model(**inputs)[0]
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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 = OPTForCausalLM(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 test_opt_sequence_classification_model(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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config.num_labels = 3
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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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sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
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model = OPTForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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def test_opt_sequence_classification_model_for_multi_label(self):
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config, input_dict = self.model_tester.prepare_config_and_inputs()
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config.num_labels = 3
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config.problem_type = "multi_label_classification"
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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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sequence_labels = ids_tensor(
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[self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
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).to(torch.float)
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model = OPTForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
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self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
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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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@require_torch
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class OPTModelIntegrationTests(unittest.TestCase):
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@slow
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def test_inference_no_head(self):
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model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device)
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input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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with torch.no_grad():
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output = model(input_ids=input_ids).last_hidden_state
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expected_shape = torch.Size((1, 11, 512))
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self.assertEqual(output.shape, expected_shape)
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# expected value works for CPU, as well as GPU (with TF32 disabled)
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expected_slice = torch.tensor(
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[
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[-0.28726277, -1.9241608, -0.3058734],
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[-1.2737825, -0.13332152, -0.18766522],
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[0.41159445, 0.1191957, -1.3107123],
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],
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device=torch_device,
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)
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assert_tensors_close(output[0, :3, :3], expected_slice, atol=5e-5)
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@require_torch
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@slow
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class OPTEmbeddingsTest(unittest.TestCase):
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def setUp(self):
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super().setUp()
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self.path_model = "facebook/opt-350m"
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def test_load_model(self):
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try:
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_ = OPTForCausalLM.from_pretrained(self.path_model)
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except BaseException:
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self.fail("Failed loading model")
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def test_logits(self):
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model = OPTForCausalLM.from_pretrained(self.path_model)
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model = model.eval()
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tokenizer = GPT2Tokenizer.from_pretrained(self.path_model)
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prompts = [
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"Today is a beautiful day and I want to",
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"In the city of",
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"Paris is the capital of France and",
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"Computers and mobile phones have taken",
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]
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# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
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inputs = tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False)
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logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(dim=-1)
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# logits_meta = torch.load(self.path_logits_meta)
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logits_meta = torch.Tensor(
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[
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[1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
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[-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
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[0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
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[6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
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]
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)
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assert torch.allclose(logits, logits_meta, atol=1e-4)
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@slow
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class OPTGenerationTest(unittest.TestCase):
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@property
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def prompts(self):
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return [
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"Today is a beautiful day and I want",
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"In the city of",
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"Paris is the capital of France and",
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"Computers and mobile phones have taken",
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]
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def test_generation_pre_attn_layer_norm(self):
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model_id = "facebook/opt-125m"
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EXPECTED_OUTPUTS = [
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"Today is a beautiful day and I want to",
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"In the city of New York, the city",
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"Paris is the capital of France and the capital",
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"Computers and mobile phones have taken over the",
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]
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predicted_outputs = []
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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model = OPTForCausalLM.from_pretrained(model_id)
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for prompt in self.prompts:
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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generated_ids = model.generate(input_ids, max_length=10)
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generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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predicted_outputs += generated_string
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self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
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def test_batch_generation(self):
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model_id = "facebook/opt-350m"
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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model = OPTForCausalLM.from_pretrained(model_id)
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model.to(torch_device)
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tokenizer.padding_side = "left"
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# use different length sentences to test batching
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sentences = [
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"Hello, my dog is a little",
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"Today, I",
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]
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inputs = tokenizer(sentences, return_tensors="pt", padding=True)
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input_ids = inputs["input_ids"].to(torch_device)
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outputs = model.generate(
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input_ids=input_ids,
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attention_mask=inputs["attention_mask"].to(torch_device),
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)
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inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
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output_non_padded = model.generate(input_ids=inputs_non_padded)
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num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
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inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
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output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
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batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
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padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
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expected_output_sentence = [
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"Hello, my dog is a little bit of a dork.\nI'm a little bit",
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"Today, I was in the middle of a conversation with a friend about the",
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]
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self.assertListEqual(expected_output_sentence, batch_out_sentence)
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self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
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def test_generation_post_attn_layer_norm(self):
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model_id = "facebook/opt-350m"
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EXPECTED_OUTPUTS = [
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"Today is a beautiful day and I want to",
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|
"In the city of San Francisco, the city",
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|
"Paris is the capital of France and the capital",
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|
"Computers and mobile phones have taken over the",
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|
]
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|
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|
predicted_outputs = []
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tokenizer = GPT2Tokenizer.from_pretrained(model_id)
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model = OPTForCausalLM.from_pretrained(model_id)
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|
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for prompt in self.prompts:
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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|
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generated_ids = model.generate(input_ids, max_length=10)
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|
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generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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|
predicted_outputs += generated_string
|
|
|
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self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
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|
|
|
@require_torch_gpu
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|
def test_batched_nan_fp16(self):
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|
# a bug manifested starting at models facebook/opt-1.3 and larger when running batched generations,
|
|
# therefore not using a tiny model, but the smallest model the problem was seen with which is opt-1.3b.
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|
# please refer to this github thread: https://github.com/huggingface/transformers/pull/17437 for more details
|
|
model_name = "facebook/opt-1.3b"
|
|
tokenizer = GPT2Tokenizer.from_pretrained(model_name, use_fast=False, padding_side="left")
|
|
|
|
model = OPTForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).cuda()
|
|
model = model.eval()
|
|
|
|
batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt")
|
|
|
|
input_ids = batch["input_ids"].cuda()
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|
attention_mask = batch["attention_mask"].cuda()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(input_ids, attention_mask=attention_mask)
|
|
self.assertFalse(
|
|
torch.isnan(outputs.logits[0]).any().item()
|
|
) # the first logits could contain NaNs if it fails
|