# What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
GPTQ integration. # What does this PR do?
This PR adds the possibility to perform GTPQ quantization with transformers model using [optimum](https://github.com/huggingface/optimum)  library. The backend relies on [auto_gptq](https://github.com/PanQiWei/AutoGPTQ) library where we use `GTPQ` and `QuantLinear` class. 
Here's the related [PR](https://github.com/huggingface/optimum/pull/1216) on optimum side. This PR can only be merged after the optimum one. 

### Quantize model 
Unlike `bitsandbytes`, it is not feasible to quantize the weights right after loading them in order to reduce memory consumption. This is why, we need to first load the entire model and then quantize it (all done in `from_pretrained`)
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_name = "facebook/opt-125m"

tokenizer = AutoTokenizer.from_pretrained(model_name)
config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer,  group_size=128, desc_act=False)
# works also with device_map (cpu offload works but not disk offload)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, quantization_config=config)
```

### Save hf model 
We save the `quantization_config` in `model.config`
```py
# move to the device you want to save model (needed if you used device_map before)
model.to(device)
quantized_model_folder = 'opt-125m-quantized_hf'
model.save_pretrained.(save_folder)
```

### Load quantized weights
If the `model.config` has a `quantization_config` key, we will replace the layers of the model and load the quantized weights.
```py
quantized_model_from_saved = AutoModelForCausalLM.from_pretrained(quantized_model_folder,
                                                                  device_map = "auto")
```

TODO:
- [ ] merge optimum PR first
- [x] doc
# Copyright 2022 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 json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class TestTokenizationMvp(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = MvpTokenizer
    rust_tokenizer_class = MvpTokenizerFast
    test_rust_tokenizer = True
    from_pretrained_filter = filter_roberta_detectors
    # from_pretrained_kwargs = {'add_prefix_space': True}

    def setUp(self):
        super().setUp()
        vocab = [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

    def get_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        return "lower newer", "lower newer"

    @cached_property
    def default_tokenizer(self):
        return MvpTokenizer.from_pretrained("RUCAIBox/mvp")

    @cached_property
    def default_tokenizer_fast(self):
        return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")

    @require_torch
    def test_prepare_batch(self):
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt")
            self.assertIsInstance(batch, BatchEncoding)

            self.assertEqual((2, 9), batch.input_ids.shape)
            self.assertEqual((2, 9), batch.attention_mask.shape)
            result = batch.input_ids.tolist()[0]
            self.assertListEqual(expected_src_tokens, result)
            # Test that special tokens are reset

    @require_torch
    def test_prepare_batch_empty_target_text(self):
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(src_text, padding=True, return_tensors="pt")
            # check if input_ids are returned and no labels
            self.assertIn("input_ids", batch)
            self.assertIn("attention_mask", batch)
            self.assertNotIn("labels", batch)
            self.assertNotIn("decoder_attention_mask", batch)

    @require_torch
    def test_tokenizer_as_target_length(self):
        tgt_text = [
            "Summary of the text.",
            "Another summary.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            targets = tokenizer(text_target=tgt_text, max_length=32, padding="max_length", return_tensors="pt")
            self.assertEqual(32, targets["input_ids"].shape[1])

    @require_torch
    def test_prepare_batch_not_longer_than_maxlen(self):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(
                ["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt"
            )
            self.assertIsInstance(batch, BatchEncoding)
            self.assertEqual(batch.input_ids.shape, (2, 1024))

    @require_torch
    def test_special_tokens(self):
        src_text = ["A long paragraph for summarization."]
        tgt_text = [
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
            input_ids = inputs["input_ids"]
            labels = inputs["labels"]
            self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
            self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())

    def test_pretokenized_inputs(self):
        pass

    def test_embeded_special_tokens(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                sentence = "A, <mask> AllenNLP sentence."
                tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
                tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
                    sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
                )

                tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
                self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

                self.assertSequenceEqual(
                    tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )
                self.assertSequenceEqual(
                    tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )

# Copyright 2022 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 json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class TestTokenizationMvp(TokenizerTesterMixin, unittest.TestCase):
    tokenizer_class = MvpTokenizer
    rust_tokenizer_class = MvpTokenizerFast
    test_rust_tokenizer = True
    from_pretrained_filter = filter_roberta_detectors
    # from_pretrained_kwargs = {'add_prefix_space': True}

    def setUp(self):
        super().setUp()
        vocab = [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "\u0120",
            "\u0120l",
            "\u0120n",
            "\u0120lo",
            "\u0120low",
            "er",
            "\u0120lowest",
            "\u0120newer",
            "\u0120wider",
            "<unk>",
        ]
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}

        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))

    def get_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_rust_tokenizer(self, **kwargs):
        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)

    def get_input_output_texts(self, tokenizer):
        return "lower newer", "lower newer"

    @cached_property
    def default_tokenizer(self):
        return MvpTokenizer.from_pretrained("RUCAIBox/mvp")

    @cached_property
    def default_tokenizer_fast(self):
        return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")

    @require_torch
    def test_prepare_batch(self):
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt")
            self.assertIsInstance(batch, BatchEncoding)

            self.assertEqual((2, 9), batch.input_ids.shape)
            self.assertEqual((2, 9), batch.attention_mask.shape)
            result = batch.input_ids.tolist()[0]
            self.assertListEqual(expected_src_tokens, result)
            # Test that special tokens are reset

    @require_torch
    def test_prepare_batch_empty_target_text(self):
        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(src_text, padding=True, return_tensors="pt")
            # check if input_ids are returned and no labels
            self.assertIn("input_ids", batch)
            self.assertIn("attention_mask", batch)
            self.assertNotIn("labels", batch)
            self.assertNotIn("decoder_attention_mask", batch)

    @require_torch
    def test_tokenizer_as_target_length(self):
        tgt_text = [
            "Summary of the text.",
            "Another summary.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            targets = tokenizer(text_target=tgt_text, max_length=32, padding="max_length", return_tensors="pt")
            self.assertEqual(32, targets["input_ids"].shape[1])

    @require_torch
    def test_prepare_batch_not_longer_than_maxlen(self):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            batch = tokenizer(
                ["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt"
            )
            self.assertIsInstance(batch, BatchEncoding)
            self.assertEqual(batch.input_ids.shape, (2, 1024))

    @require_torch
    def test_special_tokens(self):
        src_text = ["A long paragraph for summarization."]
        tgt_text = [
            "Summary of the text.",
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
            inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
            input_ids = inputs["input_ids"]
            labels = inputs["labels"]
            self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
            self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
            self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())

    def test_pretokenized_inputs(self):
        pass

    def test_embeded_special_tokens(self):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
                sentence = "A, <mask> AllenNLP sentence."
                tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
                tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
                    sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
                )

                tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
                self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

                self.assertSequenceEqual(
                    tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )
                self.assertSequenceEqual(
                    tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
                )
