# What does this PR do?

This PR deprecates the `sharded_ddp` training arguments, since ShardedDDP has been [upstreamded to PyTorch](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/), so users can use the `fsdp` traning parameter instead.

---

~~According to fairscale([see](https://github.com/facebookresearch/fairscale)), PyTorch FSDP is the recommended method for scaling to large NN models. I think Sharded-DDP is dead and it's time to say goodbye to this library.~~

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

deprecate `sharded_ddp` training argument. # What does this PR do?

This PR deprecates the `sharded_ddp` training arguments, since ShardedDDP has been [upstreamded to PyTorch](https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/), so users can use the `fsdp` traning parameter instead.

---

~~According to fairscale([see](https://github.com/facebookresearch/fairscale)), PyTorch FSDP is the recommended method for scaling to large NN models. I think Sharded-DDP is dead and it's time to say goodbye to this library.~~

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# 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 logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"

stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class TestTheRest(TestCasePlus):
    def run_eval_tester(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()
        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
        _dump_articles(input_file_name, articles)

        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

        with patch.object(sys, "argv", testargs):
            run_generate()
            assert Path(output_file_name).exists()
            # os.remove(Path(output_file_name))

    # test one model to quickly (no-@slow) catch simple problems and do an
    # extensive testing of functionality with multiple models as @slow separately
    def test_run_eval(self):
        self.run_eval_tester(T5_TINY)

    # any extra models should go into the list here - can be slow
    @parameterized.expand([BART_TINY, MBART_TINY])
    @slow
    def test_run_eval_slow(self, model):
        self.run_eval_tester(model)

    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
    @parameterized.expand([T5_TINY, MBART_TINY])
    @slow
    def test_run_eval_search(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()

        text = {
            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
            "de": [
                "Maschinelles Lernen ist großartig, oder?",
                "Ich esse gerne Bananen",
                "Morgen ist wieder ein toller Tag!",
            ],
        }

        tmp_dir = Path(self.get_auto_remove_tmp_dir())
        score_path = str(tmp_dir / "scores.json")
        reference_path = str(tmp_dir / "val.target")
        _dump_articles(input_file_name, text["en"])
        _dump_articles(reference_path, text["de"])
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {str(input_file_name)}
            {str(output_file_name)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])

        with patch.object(sys, "argv", testargs):
            with CaptureStdout() as cs:
                run_search()
            expected_strings = [" num_beams | length_penalty", model, "Best score args"]
            un_expected_strings = ["Info"]
            if "translation" in task:
                expected_strings.append("bleu")
            else:
                expected_strings.extend(ROUGE_KEYS)
            for w in expected_strings:
                assert w in cs.out
            for w in un_expected_strings:
                assert w not in cs.out
            assert Path(output_file_name).exists()
            os.remove(Path(output_file_name))

# 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 logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()


def _dump_articles(path: Path, articles: list):
    content = "\n".join(articles)
    Path(path).open("w").writelines(content)


T5_TINY = "patrickvonplaten/t5-tiny-random"
BART_TINY = "sshleifer/bart-tiny-random"
MBART_TINY = "sshleifer/tiny-mbart"

stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class TestTheRest(TestCasePlus):
    def run_eval_tester(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()
        articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
        _dump_articles(input_file_name, articles)

        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

        with patch.object(sys, "argv", testargs):
            run_generate()
            assert Path(output_file_name).exists()
            # os.remove(Path(output_file_name))

    # test one model to quickly (no-@slow) catch simple problems and do an
    # extensive testing of functionality with multiple models as @slow separately
    def test_run_eval(self):
        self.run_eval_tester(T5_TINY)

    # any extra models should go into the list here - can be slow
    @parameterized.expand([BART_TINY, MBART_TINY])
    @slow
    def test_run_eval_slow(self, model):
        self.run_eval_tester(model)

    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
    @parameterized.expand([T5_TINY, MBART_TINY])
    @slow
    def test_run_eval_search(self, model):
        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
        output_file_name = input_file_name.parent / "utest_output.txt"
        assert not output_file_name.exists()

        text = {
            "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
            "de": [
                "Maschinelles Lernen ist großartig, oder?",
                "Ich esse gerne Bananen",
                "Morgen ist wieder ein toller Tag!",
            ],
        }

        tmp_dir = Path(self.get_auto_remove_tmp_dir())
        score_path = str(tmp_dir / "scores.json")
        reference_path = str(tmp_dir / "val.target")
        _dump_articles(input_file_name, text["en"])
        _dump_articles(reference_path, text["de"])
        task = "translation_en_to_de" if model == T5_TINY else "summarization"
        testargs = f"""
            run_eval_search.py
            {model}
            {str(input_file_name)}
            {str(output_file_name)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
        testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"])

        with patch.object(sys, "argv", testargs):
            with CaptureStdout() as cs:
                run_search()
            expected_strings = [" num_beams | length_penalty", model, "Best score args"]
            un_expected_strings = ["Info"]
            if "translation" in task:
                expected_strings.append("bleu")
            else:
                expected_strings.extend(ROUGE_KEYS)
            for w in expected_strings:
                assert w in cs.out
            for w in un_expected_strings:
                assert w not in cs.out
            assert Path(output_file_name).exists()
            os.remove(Path(output_file_name))
