579 lines
24 KiB
Python
579 lines
24 KiB
Python
# Copyright 2020 The HuggingFace 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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import unittest
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import pytest
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from transformers import GPT2Config, is_torch_available
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from transformers.testing_utils import (
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Expectations,
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cleanup,
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require_flash_attn,
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require_torch,
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require_torch_gpu,
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slow,
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torch_device,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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from ...test_modeling_common import floats_tensor, 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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GPT2DoubleHeadsModel,
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GPT2ForQuestionAnswering,
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GPT2ForSequenceClassification,
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GPT2ForTokenClassification,
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GPT2LMHeadModel,
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GPT2Model,
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GPT2Tokenizer,
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)
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class GPT2ModelTester(CausalLMModelTester):
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if is_torch_available():
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config_class = GPT2Config
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base_model_class = GPT2Model
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causal_lm_class = GPT2LMHeadModel
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sequence_classification_class = GPT2ForSequenceClassification
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token_classification_class = GPT2ForTokenClassification
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question_answering_class = GPT2ForQuestionAnswering
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def __init__(
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self,
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parent,
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use_token_type_ids=True,
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num_choices=4,
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**kwargs,
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):
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super().__init__(parent, use_token_type_ids=use_token_type_ids, **kwargs)
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self.num_choices = num_choices
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def prepare_config_and_inputs(
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self, extra_inputs=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
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):
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# Overwritten: `GPT2DoubleHeadsModel` uses extra inputs
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(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) = (
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super().prepare_config_and_inputs()
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)
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if extra_inputs:
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mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
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head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
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config_and_inputs = (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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mc_token_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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else:
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config_and_inputs = (
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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)
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config = self.get_config(
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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)
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return config_and_inputs
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def get_config(self, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False):
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# Overwritten: `GPT2Config` has extra flags and we want to test them
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config = super().get_config()
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config.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
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config.reorder_and_upcast_attn = reorder_and_upcast_attn
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return config
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def prepare_config_and_inputs_for_common(self):
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# Overwritten: we want `token_type_ids` as part of the common inputs
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config_and_inputs = self.prepare_config_and_inputs(extra_inputs=True)
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config, input_ids, _, head_mask, token_type_ids, _, _, _, _ = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}
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return config, inputs_dict
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def prepare_config_and_inputs_for_decoder(self):
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# Extra function: used in `encoder_decoder` tests
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(
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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_,
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sequence_labels,
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token_labels,
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choice_labels,
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) = self.prepare_config_and_inputs(extra_inputs=True)
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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config,
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input_ids,
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input_mask,
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head_mask,
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token_type_ids,
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sequence_labels,
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token_labels,
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choice_labels,
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encoder_hidden_states,
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encoder_attention_mask,
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)
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@require_torch
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class GPT2ModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(
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GPT2Model,
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GPT2LMHeadModel,
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GPT2DoubleHeadsModel,
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GPT2ForQuestionAnswering,
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GPT2ForSequenceClassification,
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GPT2ForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": GPT2Model,
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"question-answering": GPT2ForQuestionAnswering,
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"text-classification": GPT2ForSequenceClassification,
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"text-generation": GPT2LMHeadModel,
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"token-classification": GPT2ForTokenClassification,
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"zero-shot": GPT2ForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else ()
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fx_compatible = False # Broken by attention refactor cc @Cyrilvallez
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test_missing_keys = False
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test_model_parallel = True
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model_tester_class = GPT2ModelTester
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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# Overwritten: special case for DoubleHeads model
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "GPT2DoubleHeadsModel":
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["input_ids"] = inputs_dict["labels"]
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inputs_dict["token_type_ids"] = inputs_dict["labels"]
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inputs_dict["mc_token_ids"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.num_choices),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["mc_labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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return inputs_dict
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def test_gpt2_double_lm_head_model(self):
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# extra test: model-specific class
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config_and_inputs = self.model_tester.prepare_config_and_inputs(extra_inputs=True)
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config, input_ids, input_mask, _, token_type_ids, mc_token_ids, _, _, _ = config_and_inputs
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model = GPT2DoubleHeadsModel(config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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multiple_choice_token_type_ids = (
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token_type_ids.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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)
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"mc_token_ids": mc_token_ids,
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"attention_mask": multiple_choice_input_mask,
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"token_type_ids": multiple_choice_token_type_ids,
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"labels": multiple_choice_inputs_ids,
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}
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result = model(**inputs)
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self.assertEqual(result.loss.shape, ())
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self.assertEqual(
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result.logits.shape,
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(
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self.model_tester.batch_size,
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self.model_tester.num_choices,
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self.model_tester.seq_length,
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self.model_tester.vocab_size,
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),
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)
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self.assertEqual(result.mc_logits.shape, (self.model_tester.batch_size, self.model_tester.num_choices))
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def test_gpt2_scale_attn_by_inverse_layer_idx(self):
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# extra test: model-specific flag
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config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
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config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
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model = GPT2LMHeadModel(config)
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model.to(torch_device)
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.assertEqual(result.loss.shape, ())
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self.assertEqual(
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result.logits.shape,
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(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.vocab_size),
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)
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result.loss.backward()
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def test_gpt2_reorder_and_upcast_attn(self):
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# extra test: model-specific flag
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config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True)
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config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
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model = GPT2LMHeadModel(config)
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model.to(torch_device)
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result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
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self.assertEqual(result.loss.shape, ())
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self.assertEqual(
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result.logits.shape,
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(self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.vocab_size),
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)
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result.loss.backward()
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def test_training_gradient_checkpointing(self):
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# overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
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self.original_all_model_classes = self.all_model_classes
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self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
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super().test_training_gradient_checkpointing()
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self.all_model_classes = self.original_all_model_classes
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def test_training_gradient_checkpointing_use_reentrant(self):
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# overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
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self.original_all_model_classes = self.all_model_classes
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self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
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super().test_training_gradient_checkpointing_use_reentrant()
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self.all_model_classes = self.original_all_model_classes
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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# overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
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self.original_all_model_classes = self.all_model_classes
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self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
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super().test_training_gradient_checkpointing_use_reentrant_false()
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self.all_model_classes = self.original_all_model_classes
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@require_torch
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class GPT2ModelLanguageGenerationTest(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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cleanup(torch_device, gc_collect=True)
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def _test_lm_generate_gpt2_helper(
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self,
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gradient_checkpointing=False,
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reorder_and_upcast_attn=False,
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scale_attn_by_inverse_layer_idx=False,
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verify_outputs=True,
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):
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model = GPT2LMHeadModel.from_pretrained(
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"openai-community/gpt2",
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reorder_and_upcast_attn=reorder_and_upcast_attn,
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scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
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)
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if gradient_checkpointing:
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model.gradient_checkpointing_enable()
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else:
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model.gradient_checkpointing_disable()
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model.to(torch_device)
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# The dog
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input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)
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# The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
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expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,] # fmt: skip
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output_ids = model.generate(input_ids, do_sample=False, max_length=20)
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if verify_outputs:
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self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
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@slow
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def test_lm_generate_gpt2(self):
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self._test_lm_generate_gpt2_helper()
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@slow
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def test_lm_generate_gpt2_with_gradient_checkpointing(self):
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self._test_lm_generate_gpt2_helper(gradient_checkpointing=True)
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@slow
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def test_lm_generate_gpt2_with_reorder_and_upcast_attn(self):
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self._test_lm_generate_gpt2_helper(reorder_and_upcast_attn=True)
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@slow
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def test_lm_generate_gpt2_with_scale_attn_by_inverse_layer_idx(self):
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self._test_lm_generate_gpt2_helper(scale_attn_by_inverse_layer_idx=True, verify_outputs=False)
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@slow
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def test_gpt2_sample(self):
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tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
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model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
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model.to(torch_device)
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torch.manual_seed(0)
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tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
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input_ids = tokenized.input_ids.to(torch_device)
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output_ids = model.generate(input_ids, do_sample=True, max_length=20)
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output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
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token_type_ids = tokenized.token_type_ids.to(torch_device)
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output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5, max_length=20)
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output_seq_tt = model.generate(
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input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5, max_length=20
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)
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output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
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output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
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expected_outputs = Expectations(
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{
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("rocm", None): 'Today is a nice day and we can do this again."\n\nDana said that she will',
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("rocm", (9, 5)): "Today is a nice day and if you don't know anything about the state of play during your holiday",
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("cuda", None): "Today is a nice day and if you don't know anything about the state of play during your holiday",
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("xpu", 3): "Today is a nice day and if you don't know anything about the state of play during your holiday",
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}
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) # fmt: skip
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EXPECTED_OUTPUT = expected_outputs.get_expectation()
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self.assertEqual(output_str, EXPECTED_OUTPUT)
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self.assertTrue(
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all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs)))
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) # token_type_ids should change output
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# TODO joao, manuel: remove this in v4.62.0
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@slow
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def test_contrastive_search_gpt2(self):
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article = (
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"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research "
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"laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based"
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)
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gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large")
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gpt2_model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large").to(torch_device)
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input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device)
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outputs = gpt2_model.generate(
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input_ids,
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penalty_alpha=0.6,
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top_k=4,
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max_length=256,
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trust_remote_code=True,
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custom_generate="transformers-community/contrastive-search",
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)
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generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True)
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self.assertListEqual(
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generated_text,
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[
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"DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research "
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"laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, "
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"United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as "
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"Google Now, which helps users find the information they're looking for on the web. But the company "
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"is not the only one to collect data on its users. Facebook, for example, has its own facial "
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"recognition technology, as well as a database of millions of photos that it uses to personalize its "
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"News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates "
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"concerned about the company's ability to keep users' information private. In a blog post last "
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'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our '
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'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with '
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'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at '
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'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, '
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"but said in a statement to The Associated Press that"
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],
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)
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@require_flash_attn
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@require_torch_gpu
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@pytest.mark.flash_attn_test
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@slow
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def test_flash_attn_2_generate_padding_left(self):
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"""
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Overwriting the common test as the test is flaky on tiny models
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"""
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model = GPT2LMHeadModel.from_pretrained("gpt2", dtype=torch.float16).to(0)
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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texts = ["hi", "Hello this is a very long sentence"]
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tokenizer.padding_side = "left"
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tokenizer.pad_token = tokenizer.eos_token
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|
|
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0)
|
|
|
|
output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_native = tokenizer.batch_decode(output_native)
|
|
|
|
model = GPT2LMHeadModel.from_pretrained(
|
|
"gpt2", device_map={"": 0}, attn_implementation="flash_attention_2", dtype=torch.float16
|
|
)
|
|
|
|
output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False)
|
|
output_fa_2 = tokenizer.batch_decode(output_fa_2)
|
|
|
|
expected_output = [
|
|
"<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>hi, who was born in the city of Kolkata, was a member of the Kolkata",
|
|
"Hello this is a very long sentence. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry",
|
|
]
|
|
|
|
self.assertListEqual(output_native, output_fa_2)
|
|
self.assertListEqual(output_native, expected_output)
|
|
|
|
@slow
|
|
def test_batch_generation(self):
|
|
model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
|
|
model.to(torch_device)
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
|
|
|
tokenizer.padding_side = "left"
|
|
|
|
# Define PAD Token = EOS Token = 50256
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
model.config.pad_token_id = model.config.eos_token_id
|
|
|
|
# use different length sentences to test batching
|
|
sentences = [
|
|
"Hello, my dog is a little",
|
|
"Today, I",
|
|
]
|
|
|
|
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
|
input_ids = inputs["input_ids"].to(torch_device)
|
|
token_type_ids = torch.cat(
|
|
[
|
|
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
|
|
input_ids.new_full((input_ids.shape[0], 1), 500),
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
outputs = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
max_length=20,
|
|
)
|
|
|
|
outputs_tt = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
token_type_ids=token_type_ids,
|
|
max_length=20,
|
|
)
|
|
|
|
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
|
|
output_non_padded = model.generate(input_ids=inputs_non_padded, max_length=20)
|
|
|
|
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().item()
|
|
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
|
|
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
|
|
|
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
|
|
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
|
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
|
|
|
expected_output_sentence = [
|
|
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
|
|
"Today, I'm going to be doing a lot of research on this. I",
|
|
]
|
|
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
|
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
|
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|
|
|
|
@slow
|
|
def test_batch_generation_2heads(self):
|
|
model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2")
|
|
model.to(torch_device)
|
|
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
|
|
|
|
tokenizer.padding_side = "left"
|
|
|
|
# This tokenizer has no pad token, so we have to set it in some way
|
|
# Define PAD Token = EOS Token = 50256
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
model.config.pad_token_id = model.config.eos_token_id
|
|
|
|
# use different length sentences to test batching
|
|
sentences = [
|
|
"Hello, my dog is a little",
|
|
"Today, I",
|
|
]
|
|
|
|
inputs = tokenizer(sentences, return_tensors="pt", padding=True)
|
|
input_ids = inputs["input_ids"].to(torch_device)
|
|
token_type_ids = torch.cat(
|
|
[
|
|
input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0),
|
|
input_ids.new_full((input_ids.shape[0], 1), 500),
|
|
],
|
|
dim=-1,
|
|
)
|
|
|
|
outputs = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
max_length=20,
|
|
)
|
|
|
|
outputs_tt = model.generate(
|
|
input_ids=input_ids,
|
|
attention_mask=inputs["attention_mask"].to(torch_device),
|
|
token_type_ids=token_type_ids,
|
|
max_length=20,
|
|
)
|
|
|
|
inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
|
|
output_non_padded = model.generate(input_ids=inputs_non_padded, max_length=20)
|
|
|
|
num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().item()
|
|
inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
|
|
output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
|
|
|
|
batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
|
|
batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True)
|
|
non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
|
|
padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
|
|
|
|
expected_output_sentence = [
|
|
"Hello, my dog is a little bit of a mess. I'm not sure if he's going",
|
|
"Today, I'm going to be doing a lot of research on this. I",
|
|
]
|
|
self.assertListEqual(expected_output_sentence, batch_out_sentence)
|
|
self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output
|
|
self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
|