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tests/models/gpt2/test_modeling_gpt2.py

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+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import pytest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+from ...test_modeling_common import floats_tensor, ids_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        GPT2DoubleHeadsModel,
+        GPT2ForQuestionAnswering,
+        GPT2ForSequenceClassification,
+        GPT2ForTokenClassification,
+        GPT2LMHeadModel,
+        GPT2Model,
+        GPT2Tokenizer,
+    )
+
+
+class GPT2ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = GPT2Model
+        causal_lm_class = GPT2LMHeadModel
+
+    def __init__(
+        self,
+        parent,
+        use_token_type_ids=True,
+        num_choices=4,
+        **kwargs,
+    ):
+        super().__init__(parent, use_token_type_ids=use_token_type_ids, **kwargs)
+        self.num_choices = num_choices
+
+    def prepare_config_and_inputs(
+        self, extra_inputs=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False
+    ):
+        # Overwritten: `GPT2DoubleHeadsModel` uses extra inputs
+        (config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) = (
+            super().prepare_config_and_inputs()
+        )
+
+        if extra_inputs:
+            mc_token_ids = ids_tensor([self.batch_size, self.num_choices], self.seq_length)
+            config_and_inputs = (
+                config,
+                input_ids,
+                input_mask,
+                token_type_ids,
+                mc_token_ids,
+                sequence_labels,
+                token_labels,
+                choice_labels,
+            )
+        else:
+            config_and_inputs = (
+                config,
+                input_ids,
+                token_type_ids,
+                input_mask,
+                sequence_labels,
+                token_labels,
+                choice_labels,
+            )
+
+        config = self.get_config(
+            scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
+            reorder_and_upcast_attn=reorder_and_upcast_attn,
+        )
+
+        return config_and_inputs
+
+    def get_config(self, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False):
+        # Overwritten: `GPT2Config` has extra flags and we want to test them
+        config = super().get_config()
+        config.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+        config.reorder_and_upcast_attn = reorder_and_upcast_attn
+        return config
+
+    def prepare_config_and_inputs_for_common(self):
+        # Overwritten: we want `token_type_ids` as part of the common inputs
+        config_and_inputs = self.prepare_config_and_inputs(extra_inputs=True)
+        config, input_ids, attention_mask, token_type_ids, _, _, _, _ = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "token_type_ids": token_type_ids}
+        return config, inputs_dict
+
+    def prepare_config_and_inputs_for_decoder(self):
+        # Extra function: used in `encoder_decoder` tests
+        (
+            config,
+            input_ids,
+            input_mask,
+            token_type_ids,
+            _,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs(extra_inputs=True)
+
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            input_mask,
+            token_type_ids,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+
+@require_torch
+class GPT2ModelTest(CausalLMModelTest, unittest.TestCase):
+    # `all_model_classes` is overwritten because of `GPT2DoubleHeadsModel`
+    all_model_classes = (
+        (
+            GPT2Model,
+            GPT2LMHeadModel,
+            GPT2DoubleHeadsModel,
+            GPT2ForQuestionAnswering,
+            GPT2ForSequenceClassification,
+            GPT2ForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    # We need to set `pipeline_model_mapping` because we overwrite `all_model_classes`
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GPT2Model,
+            "text-classification": GPT2ForSequenceClassification,
+            "text-generation": GPT2LMHeadModel,
+            "token-classification": GPT2ForTokenClassification,
+            "zero-shot": GPT2ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_missing_keys = False
+    model_tester_class = GPT2ModelTester
+    model_split_percents = [0.5, 0.6, 0.7]
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        # Overwritten: special case for DoubleHeads model
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+
+        if return_labels:
+            if model_class.__name__ == "GPT2DoubleHeadsModel":
+                inputs_dict["labels"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length),
+                    dtype=torch.long,
+                    device=torch_device,
+                )
+                inputs_dict["input_ids"] = inputs_dict["labels"]
+                inputs_dict["attention_mask"] = torch.tril(torch.ones_like(inputs_dict["input_ids"]).to(torch_device))
+                inputs_dict["token_type_ids"] = inputs_dict["labels"]
+                inputs_dict["mc_token_ids"] = torch.zeros(
+                    (self.model_tester.batch_size, self.model_tester.num_choices),
+                    dtype=torch.long,
+                    device=torch_device,
+                )
+                inputs_dict["mc_labels"] = torch.zeros(
+                    self.model_tester.batch_size, dtype=torch.long, device=torch_device
+                )
+        return inputs_dict
+
+    def test_gpt2_double_lm_head_model(self):
+        # extra test: model-specific class
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(extra_inputs=True)
+        config, input_ids, input_mask, token_type_ids, mc_token_ids, _, _, _ = config_and_inputs
+        model = GPT2DoubleHeadsModel(config)
+        model.to(torch_device)
+        model.eval()
+
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
+        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
+        multiple_choice_token_type_ids = (
+            token_type_ids.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
+        )
+
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "mc_token_ids": mc_token_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+            "labels": multiple_choice_inputs_ids,
+        }
+
+        result = model(**inputs)
+        self.assertEqual(result.loss.shape, ())
+        self.assertEqual(
+            result.logits.shape,
+            (
+                self.model_tester.batch_size,
+                self.model_tester.num_choices,
+                self.model_tester.seq_length,
+                self.model_tester.vocab_size,
+            ),
+        )
+        self.assertEqual(result.mc_logits.shape, (self.model_tester.batch_size, self.model_tester.num_choices))
+
+    def test_gpt2_scale_attn_by_inverse_layer_idx(self):
+        # extra test: model-specific flag
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
+        config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
+
+        model = GPT2LMHeadModel(config)
+        model.to(torch_device)
+        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+        self.assertEqual(result.loss.shape, ())
+        self.assertEqual(
+            result.logits.shape,
+            (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.vocab_size),
+        )
+        result.loss.backward()
+
+    def test_gpt2_sdpa_matches_eager_with_scaling_configs(self):
+        """Test that SDPA and eager produce equivalent outputs when scaling configs differ from defaults.
+
+        Regression test for https://github.com/huggingface/transformers/issues/44380
+        """
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
+        config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
+        config.scale_attn_weights = False
+        config.scale_attn_by_inverse_layer_idx = True
+
+        model = GPT2LMHeadModel(config).to(torch_device).eval()
+
+        # Eager attention (known-correct reference)
+        model.set_attn_implementation("eager")
+        with torch.no_grad():
+            output_eager = model(input_ids, token_type_ids=token_type_ids).logits
+
+        # SDPA attention (was buggy: ignored scaling configs)
+        model.set_attn_implementation("sdpa")
+        with torch.no_grad():
+            output_sdpa = model(input_ids, token_type_ids=token_type_ids).logits
+
+        torch.testing.assert_close(output_eager, output_sdpa, atol=1e-4, rtol=1e-4)
+
+    @require_torch_gpu
+    @require_flash_attn
+    @pytest.mark.flash_attn_test
+    def test_gpt2_fa2_matches_eager_with_scaling_configs(self):
+        """Test that FlashAttention2 and eager produce equivalent outputs when scaling configs differ.
+
+        Regression test for https://github.com/huggingface/transformers/issues/44380
+        """
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True)
+        config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
+        config.scale_attn_weights = False
+        config.scale_attn_by_inverse_layer_idx = True
+
+        model = GPT2LMHeadModel(config).to(torch_device).eval().to(torch.float16)
+        input_ids = input_ids.to(torch_device)
+        token_type_ids = token_type_ids.to(torch_device)
+
+        # Eager attention (known-correct reference)
+        model.set_attn_implementation("eager")
+        with torch.no_grad():
+            output_eager = model(input_ids, token_type_ids=token_type_ids).logits
+
+        # Flash Attention 2 (was buggy: ignored scaling configs)
+        model.set_attn_implementation("flash_attention_2")
+        with torch.no_grad():
+            output_fa2 = model(input_ids, token_type_ids=token_type_ids).logits
+
+        torch.testing.assert_close(output_eager, output_fa2, atol=1e-2, rtol=1e-2)
+
+    def test_gpt2_reorder_and_upcast_attn(self):
+        # extra test: model-specific flag
+        config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True)
+        config, input_ids, token_type_ids, _, _, _, _ = config_and_inputs
+
+        model = GPT2LMHeadModel(config)
+        model.to(torch_device)
+        result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids)
+        self.assertEqual(result.loss.shape, ())
+        self.assertEqual(
+            result.logits.shape,
+            (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.vocab_size),
+        )
+        result.loss.backward()
+
+    def test_training_gradient_checkpointing(self):
+        # overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
+        self.original_all_model_classes = self.all_model_classes
+        self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
+        super().test_training_gradient_checkpointing()
+        self.all_model_classes = self.original_all_model_classes
+
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        # overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
+        self.original_all_model_classes = self.all_model_classes
+        self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
+        super().test_training_gradient_checkpointing_use_reentrant_false()
+        self.all_model_classes = self.original_all_model_classes
+
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        # overwritten: GPT2DoubleHeadsModel fails this test, non-standard class
+        self.original_all_model_classes = self.all_model_classes
+        self.all_model_classes = (cls for cls in self.all_model_classes if cls.__name__ != "GPT2DoubleHeadsModel")
+        super().test_training_gradient_checkpointing_use_reentrant_true()
+        self.all_model_classes = self.original_all_model_classes
+
+
+@require_torch
+class GPT2ModelLanguageGenerationTest(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        # clean-up as much as possible GPU memory occupied by PyTorch
+        cleanup(torch_device, gc_collect=True)
+
+    def _test_lm_generate_gpt2_helper(
+        self,
+        gradient_checkpointing=False,
+        reorder_and_upcast_attn=False,
+        scale_attn_by_inverse_layer_idx=False,
+        verify_outputs=True,
+    ):
+        model = GPT2LMHeadModel.from_pretrained(
+            "openai-community/gpt2",
+            reorder_and_upcast_attn=reorder_and_upcast_attn,
+            scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx,
+        )
+        if gradient_checkpointing:
+            model.gradient_checkpointing_enable()
+        else:
+            model.gradient_checkpointing_disable()
+        model.to(torch_device)
+
+        # The dog
+        input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)
+
+        # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
+        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
+        output_ids = model.generate(input_ids, do_sample=False, max_length=20)
+        if verify_outputs:
+            self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
+
+    @slow
+    def test_lm_generate_gpt2(self):
+        self._test_lm_generate_gpt2_helper()
+
+    @slow
+    def test_lm_generate_gpt2_with_gradient_checkpointing(self):
+        self._test_lm_generate_gpt2_helper(gradient_checkpointing=True)
+
+    @slow
+    def test_lm_generate_gpt2_with_reorder_and_upcast_attn(self):
+        self._test_lm_generate_gpt2_helper(reorder_and_upcast_attn=True)
+
+    @slow
+    def test_lm_generate_gpt2_with_scale_attn_by_inverse_layer_idx(self):
+        self._test_lm_generate_gpt2_helper(scale_attn_by_inverse_layer_idx=True, verify_outputs=False)
+
+    @slow
+    def test_gpt2_sample(self):
+        tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
+        model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2")
+        model.to(torch_device)
+
+        torch.manual_seed(0)
+        tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True)
+        input_ids = tokenized.input_ids.to(torch_device)
+        output_ids = model.generate(input_ids, do_sample=True, max_length=20)
+        output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+        token_type_ids = tokenized.token_type_ids.to(torch_device)
+        output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5, max_length=20)
+        output_seq_tt = model.generate(
+            input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5, max_length=20
+        )
+        output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True)
+        output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True)
+
+        expected_outputs = Expectations(
+            {
+                ("rocm", None): 'Today is a nice day and we can do this again."\n\nDana said that she will',
+                ("rocm", (9, 5)): "Today is a nice day and if you don't know anything about the state of play during your holiday",
+                ("cuda", None): "Today is a nice day and if you don't know anything about the state of play during your holiday",
+                ("xpu", 3): "Today is a nice day and if you don't know anything about the state of play during your holiday",
+            }
+        )  # fmt: skip
+        EXPECTED_OUTPUT = expected_outputs.get_expectation()
+        self.assertEqual(output_str, EXPECTED_OUTPUT)
+        self.assertTrue(
+            all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs)))
+        )  # token_type_ids should change output
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_generate_padding_left(self):
+        """
+        Overwriting the common test as the test is flaky on tiny models
+        """
+        model = GPT2LMHeadModel.from_pretrained("gpt2", dtype=torch.float16).to(0)
+
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+        texts = ["hi", "Hello this is a very long sentence"]
+
+        tokenizer.padding_side = "left"
+        tokenizer.pad_token = tokenizer.eos_token
+
+        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 = Expectations(
+            {
+                ("cuda", (8, 6)): [
+                    "<|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",
+                ],
+                ("rocm", (9, 4)): [
+                    '<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>hi, who was also a member of the group, said: "We are very happy to have been',
+                    "Hello this is a very long sentence. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry",
+                ],
+            }
+        ).get_expectation()
+
+        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"
+        max_length = 20
+
+        # 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=max_length,
+        )
+
+        outputs_tt = model.generate(
+            input_ids=input_ids,
+            attention_mask=inputs["attention_mask"].to(torch_device),
+            token_type_ids=token_type_ids,
+            max_length=max_length,
+        )
+
+        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=max_length)
+
+        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=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"
+        max_length = 20
+
+        # 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=max_length,
+        )
+
+        outputs_tt = model.generate(
+            input_ids=input_ids,
+            attention_mask=inputs["attention_mask"].to(torch_device),
+            token_type_ids=token_type_ids,
+            max_length=max_length,
+        )
+
+        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=max_length)
+
+        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=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])

tests/models/gpt2/test_tokenization_gpt2.py

@@ -0,0 +1,140 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import unittest
+
+from transformers import AutoTokenizer, GPT2Tokenizer
+from transformers.testing_utils import require_tiktoken, require_tokenizers
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+@require_tokenizers
+class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+    from_pretrained_id = ["openai-community/gpt2"]
+    tokenizer_class = GPT2Tokenizer
+    from_pretrained_kwargs = {"add_prefix_space": False}
+
+    integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<', 's', '>', 'Ċ', 'hi', '<', 's', '>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing']  # fmt: skip
+    integration_expected_token_ids = [1212, 318, 257, 1332, 30325, 232, 198, 40, 373, 4642, 287, 10190, 830, 11, 290, 428, 318, 27807, 2634, 13, 198, 37955, 162, 112, 119, 21410, 40367, 253, 164, 108, 249, 42468, 198, 17250, 220, 18435, 198, 17250, 220, 220, 18435, 628, 220, 198, 220, 220, 198, 18435, 198, 27, 82, 29, 198, 5303, 27, 82, 29, 8117, 198, 464, 1708, 4731, 815, 307, 6105, 30240, 25, 18435, 13, 198, 1537, 220, 1447, 290, 220, 19567, 249, 19567, 113, 220, 220, 220, 1447, 220, 220, 220, 19567, 242, 198, 10814, 703, 389, 345, 1804]  # fmt: skip
+    expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'Ġ', 'Ġ', 'ĠHello', 'ĊĊ', 'Ġ', 'Ċ', 'Ġ', 'Ġ', 'Ċ', 'ĠHello', 'Ċ', '<', 's', '>', 'Ċ', 'hi', '<', 's', '>', 'there', 'Ċ', 'The', 'Ġfollowing', 'Ġstring', 'Ġshould', 'Ġbe', 'Ġproperly', 'Ġencoded', ':', 'ĠHello', '.', 'Ċ', 'But', 'Ġ', 'ird', 'Ġand', 'Ġ', 'à¸', 'Ľ', 'à¸', 'µ', 'Ġ', 'Ġ', 'Ġ', 'ird', 'Ġ', 'Ġ', 'Ġ', 'à¸', 'Ķ', 'Ċ', 'Hey', 'Ġhow', 'Ġare', 'Ġyou', 'Ġdoing']  # fmt: skip
+    integration_expected_decoded_text = "This is a test 😊\nI was born in 92000, and this is falsé.\n生活的真谛是\nHi  Hello\nHi   Hello\n\n \n  \n Hello\n<s>\nhi<s>there\nThe following string should be properly encoded: Hello.\nBut ird and ปี   ird   ด\nHey how are you doing"
+
+    @unittest.skip
+    def test_pretokenized_inputs(self, *args, **kwargs):
+        # It's very difficult to mix/test pretokenization with byte-level
+        # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
+        pass
+
+    @unittest.skip(reason="tokenizer has no padding token")
+    def test_padding_different_model_input_name(self):
+        pass
+
+    def test_special_tokens_mask_input_pairs_and_bos_token(self):
+        # TODO: change to self.get_tokenizers() when the fast version is implemented
+        tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)]
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                sequence_0 = "Encode this."
+                sequence_1 = "This one too please."
+                encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False)
+                encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False)
+                encoded_sequence_dict = tokenizer(
+                    sequence_0,
+                    sequence_1,
+                    add_special_tokens=True,
+                    return_special_tokens_mask=True,
+                )
+                encoded_sequence_w_special = encoded_sequence_dict["input_ids"]
+                special_tokens_mask = encoded_sequence_dict["special_tokens_mask"]
+                self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special))
+
+                filtered_sequence = [
+                    (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special)
+                ]
+                filtered_sequence = [x for x in filtered_sequence if x is not None]
+                self.assertEqual(encoded_sequence, filtered_sequence)
+
+    @require_tiktoken
+    def test_tokenization_tiktoken(self):
+        from tiktoken import encoding_name_for_model
+
+        from transformers.integrations.tiktoken import convert_tiktoken_to_fast
+
+        encoding = encoding_name_for_model("gpt2")
+        convert_tiktoken_to_fast(encoding, self.tmpdirname)
+
+        tiktoken_fast_tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname)
+        rust_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")
+        sequence = "lower newer"
+        self.assertEqual(
+            rust_tokenizer.decode(rust_tokenizer.encode(sequence)),
+            tiktoken_fast_tokenizer.decode(rust_tokenizer.encode(sequence)),
+        )
+
+
+@require_tokenizers
+class OPTTokenizationTest(unittest.TestCase):
+    def test_serialize_deserialize_fast_opt(self):
+        # More context:
+        # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
+        # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
+        # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
+
+        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+        text = "A photo of a cat"
+
+        tokens_ids = tokenizer.encode(
+            text,
+        )
+        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])
+        tokenizer.save_pretrained("test_opt")
+
+        tokenizer = AutoTokenizer.from_pretrained("./test_opt")
+        tokens_ids = tokenizer.encode(
+            text,
+        )
+        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])
+
+    def test_fast_slow_equivalence(self):
+        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+        text = "A photo of a cat"
+
+        tokens_ids = tokenizer.encode(
+            text,
+        )
+        # Same as above
+        self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758])
+
+    @unittest.skip(reason="This test is failing because of a bug in the fast tokenizer")
+    def test_users_can_modify_bos(self):
+        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+        tokenizer.bos_token = "bos"
+        tokenizer.bos_token_id = tokenizer.get_vocab()["bos"]
+
+        text = "A photo of a cat"
+        tokens_ids = tokenizer.encode(
+            text,
+        )
+        # We changed the bos token
+        self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])
+        tokenizer.save_pretrained("./tok")
+        tokenizer = AutoTokenizer.from_pretrained("./tok")
+        self.assertTrue(tokenizer.is_fast)
+        tokens_ids = tokenizer.encode(
+            text,
+        )
+        self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])