Summary:
## What does this PR do?
Some downstream users reported that errors when passing Namespace to load_checkpoint().
A recent change made the assumption that the passed object is dict like (dict or DictConfig) that have a get function.
This changes that and make sure the mocked config have checkpoint.save_dir to allow the test to run.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3675
Reviewed By: omry
Differential Revision: D29564805
Pulled By: lematt1991
fbshipit-source-id: 89308811da382667f6c5d3152ee2d6480416ee62
Summary:
# Before submitting
`self.task.target_dictionary` is queried before `task_state` is loaded (in `self.load_model_ensemble()`).
## What does this PR do?
Fix the bug above
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2019
Reviewed By: alexeib
Differential Revision: D29523921
Pulled By: wnhsu
fbshipit-source-id: 763b504dc1b4899e623eaa5c19972cec9d0a8985
Summary:
fixes an issue with some old checkpoints that had deep nested namespaces containing choices enum - most prominently xlsr 53 checkpoint
fixes#3634
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/2013
Reviewed By: xuqiantong
Differential Revision: D29511325
Pulled By: alexeib
fbshipit-source-id: 79df978afa7482b4ce3aaf7396e193626181aa17
Summary:
## What does this PR do?
Fixes compatibility with Hydra 1.1.
The result is compatible with both Hydra 1.0 and Hydra 1.1, and will allow a smoother migration to Hydra 1.1.
At this point I am not yet removing the restriction on the Hydra version from setup.py:
1. It depends on some Hydra 1.1 changes that are not yet released (It will be compatible with 1.1.1).
2. Upgrading will result in deprecation warnings, and fixing them will break compatibility with Hydra 1.0.
There will be some followup to make the code fully compatible with 1.1 once Hydra 1.1 is the default version in fbcode.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3659
Reviewed By: omry
Differential Revision: D29498036
Pulled By: lematt1991
fbshipit-source-id: 96999cde5daad6749ef4d3ddf6a36a1e984ff201
Summary:
## What does this PR do?
there are a few places where we do file chunking for multiprocessing a single file. However, the code is partly in Binarizer and partly just duplicated here and there.
This PR extracts the file chunking/reading logic. The multiprocessing logic could probably be extracted too, but I haven't found a good abstraction yet.
# Testing
Added testing for this reading logic + maybe fixed a bug where the last part of a file might get dropped (even if it's unclear with the current stopping logic)
Tested by running the preprocessing script as follow:
```
python -m fairseq_cli.preprocess --source-lang de --target-lang en --trainpref ...train.spm.clean.de_en --srcdict ...fairseq.dict --tgtdict .../fairseq.dict --destdir ... --workers 60
```
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1955
Reviewed By: myleott
Differential Revision: D29065473
Pulled By: Mortimerp9
fbshipit-source-id: c60843de8cfd45a63b3dbb8290f57ef3df3bf983
Summary:
## What does this PR do?
Load dict from pretrained hubert model in HubertEncoder so that the dictionary is not constructed for the labels.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1999
Test Plan:
Tested with the cmdline below. ASR training progresses as expected without any exception.
```
PYTHONPATH=. HYDRA_FULL_ERROR=1 python fairseq_cli/hydra_train.py -m \
--config-dir examples/hubert/config/finetune \
--config-name base_10h \
dataset.num_workers=0 \
task.data=/checkpoint/kushall/data/librispeech/10h/raw \
task.label_dir=/checkpoint/kushall/data/librispeech/10h/raw \
model.w2v_path=/checkpoint/kushall/final_model_checkpoints/hubert/hubert_base_ls960_updated.pt \
hydra.sweep.dir=/checkpoint/kushall/experiments/hubert_test/base_asr_10h
```
Reviewed By: Abdel-rahmanMohamed
Differential Revision: D29405491
Pulled By: hikushalhere
fbshipit-source-id: be168a0ce27f8fcfea3dc980a192ba43fdf23871
Summary:
Add a decoder class `FairSeqNVFasterTransformerDecoder` that could replace `TransformerDecoder` in nmt.
Add a decoding class `FairSeqNVFasterTransformerDecoding` that does `decoding + beam serach`.
We can't use `FairSeqNVFasterTransformerDecoding` right now in nmt because nmt ensembles decoders and calculate avg probabilities across those decoders.
Follow ups:
1. Currently `FairSeqNVFasterTransformerDecoder` doesn't produce "attn" https://fburl.com/code/pom5vhr5.
2. Move mem_cache and cache to incremental_state
2. Benchmark fairseq ft encoder decoder.
2. E2e tests stucks at somewhere.
Differential Revision: D29166310
fbshipit-source-id: 36360cfff1d22ed4f12f89068ee30dec835d2141
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes https://github.com/pytorch/fairseq/issues/3371.
Currently, the output from Wav2Vec 2.0 decoding does not contain word-level start/end times, which can be useful for certain applications of ASR. Based on the discussion [here](https://github.com/flashlight/flashlight/issues/618), they could be computed based on the output from the Flashlight decoder. For the KenLM decoder, we could first obtain the frame number corresponding to each non-blank token. Next, the timestamp of each character could be computed as `segment_start + frame_no/total_frames * segment_duration`. Finally, the start and end time of each word could be calculated based on the timestamp of the word boundary characters. In order to enable this, the frame number of each non-blank character is returned as a result of KenLM decoding. This is similar to the `timesteps` output from the [ctcdecode](https://github.com/parlance/ctcdecode#outputs-from-the-decode-method) library.
## PR review
alexeib
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3627
Reviewed By: michaelauli
Differential Revision: D29282488
Pulled By: alexeib
fbshipit-source-id: b5fe64bf50abd7ef8e9539f4e338937c866eb0ca
Summary:
## What does this PR do?
Fixes issue of creating factories causing errors because the lambda function is not proper.
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1987
Test Plan:
Completed a small pretraining+finetuning procedure:
Pretraining:
```
PYTHONPATH=. python /private/home/neeyanth/project/fairseq/fairseq_cli/hydra_train.py -m \
--config-dir ${fairseq_dir}/examples/hubert/config/pretrain \
--config-name hubert_base_librispeech \
hydra/launcher=submitit_local \
hydra.launcher.gpus_per_node=1 \
hydra.launcher.cpus_per_task=8 \
hydra.launcher.mem_gb=384 \
task.data=${tsv_dir} \
task.label_dir=${km_dir} \
task.labels=["km"] \
+data=iter1 \
optimization.max_update=250 \
hydra.sweep.dir=${exp_dir} \
hydra.run.dir=${exp_dir} > ${exp_dir}/log.out 2> ${exp_dir}/log.err &
```
Finetuning:
```
PYTHONPATH=. python /private/home/neeyanth/project/fairseq/fairseq_cli/hydra_train.py -m \
--config-dir ${fairseq_dir}/examples/hubert/config/finetune \
--config-name base_10h \
hydra/launcher=submitit_local \
hydra.launcher.gpus_per_node=1 \
hydra.launcher.cpus_per_task=8 \
hydra.launcher.mem_gb=384 \
task.data=${tsv_dir} \
task.label_dir=${tsv_dir} \
model.w2v_path=${model_dir} \
+data=iter1 \
optimization.max_update=250 \
hydra.sweep.dir=${exp_dir} \
hydra.run.dir=${exp_dir} > ${exp_dir}/log.out 2> ${exp_dir}/log.err &
```
Reviewed By: hikushalhere
Differential Revision: D29266136
Pulled By: neeyanthkvk
fbshipit-source-id: d36c668ae38a7761b4c44f4dcb0c4cc8e15e42ce
Summary:
- Add average lagging latency metrics for online model. Offline models by default return 0
- Pad smaller input chunks with 0.
- Enable export option in layer norm in transformer.py to avoid errors in scripted model inference.
- Warm up prediction for scripted online model
- Add additional args like force_read_cnt, data_split
Reviewed By: xutaima
Differential Revision: D28881594
fbshipit-source-id: fd4cce017539b5d8f6e39f9af9651341e47d6db0
Summary:
warmup_init_lr should not be used if there is no warmup i.e. warmup_updates = 0
Created from Diffusion's 'Open in Editor' feature.
Reviewed By: myleott
Differential Revision: D29174059
fbshipit-source-id: c2e4cf998aebcff090584e689f692a0abe082e65
Summary:
## What does this PR do?
For HubertPretrainingTask, added dictionaries to the task state to enable the serialization of the dictionaries (thus removing the need to load from the disk after training)
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3601
Test Plan:
To verify the success, run the Hubert Pretraining pipeline, load a checkpoint model, and verify that the "dictionaries" key is present in the state within the model.
Specifically,
```
PYTHONPATH=. python /path/to/fairseq/fairseq_cli/hydra_train.py -m \
--config-dir ${fairseq_dir}/examples/hubert/config/pretrain \
--config-name hubert_base_librispeech \
hydra/launcher=submitit_local \
hydra.launcher.gpus_per_node=2 \
hydra.launcher.cpus_per_task=8 \
hydra.launcher.mem_gb=384 \
task.data=${tsv_dir} \
task.label_dir=${km_dir} \
task.labels=["km"] \
+data=iter1 \
optimization.max_update=250 \
hydra.sweep.dir=${exp_dir} \
hydra.run.dir=${exp_dir} > ${exp_dir}/log.out 2> ${exp_dir}/log.err &
```
Then, at the location of the model, load the model using `pytorch.load`, and verifying that "dictionaries" is a key under the `task_state` key of the model.
## Did you have fun?
Make sure you had fun coding �
Reviewed By: wnhsu
Differential Revision: D28995537
Pulled By: neeyanthkvk
fbshipit-source-id: e10c5163c367285518961b3ce1e719a29da06aa6
Summary:
## What does this PR do?
Fixes None exception when some attributes in don't exist in cfg.
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1970
Reviewed By: alexeib
Differential Revision: D29140036
Pulled By: hikushalhere
fbshipit-source-id: 7d941bcae6bb000c281a43ca2cd0876a49912ab9
Summary:
## What does this PR do?
Fixes the HuBERT README to contain instructions to load pretrained checkpoints.
## PR review
Tested in a fresh environment that doesn't have access to FAIR's dev env.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1966
Reviewed By: wnhsu
Differential Revision: D29117906
Pulled By: hikushalhere
fbshipit-source-id: 89b0407ecf8cdbeddcab80f55e6b2f1fed24c967
Summary:
In EpochBatchIterator, first_batch() freezes batches in order to
generate the dummy_batch. We then freeze batches again in the call
to next_epoch_itr(). We can avoid the second freeze and reduce
time to first iteration by about 50% in cases where we have a
callable batch_sampler.
Before:
After:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes # (issue).
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3610
Reviewed By: myleott
Differential Revision: D29105845
Pulled By: msbaines
fbshipit-source-id: 9795d46d70a99ad1218ce225092cc22ee3192bbc
Summary:
Fairseq create an empty tensor without type.
It will create warning for torchscript model.
Warning: Creating a tensor from an empty intlist will create a tensor of default floating point type (currently Float) in python but a tensor of type int in torchscript.
This diff adds definition of the type.
Reviewed By: myleott
Differential Revision: D29081170
fbshipit-source-id: 5c32aae65c9998b245eac43bfedc820bea509338
Summary:
If we do the FP16/BF32 conversion on the host, we do it at DRAM speed but transfer 2X smaller buffer to the GPU through PCIe. PCIe bandwidth is an order of magnitude lower so we actually gain about 50% of execution time compared to when performing the quantization on the GPU.
Also, by transfering an already FP16 buffer, we save memory capacity.
Reviewed By: zhengwy888
Differential Revision: D24146486
fbshipit-source-id: b897e7a32835aa1b571b0fae5f3d72a131ad16a1
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes # (issue).
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3593
Reviewed By: msbaines
Differential Revision: D28992614
Pulled By: dianaml0
fbshipit-source-id: b2dfcab472a65c41536e78600a0e6b3745dc3a08
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes # (issue).
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1944
Reviewed By: jingfeidu
Differential Revision: D28944206
fbshipit-source-id: 583837f7dd387341574d27dd9acc145455d640a8
Summary:
D28728718 cleaned up the "kd_binary_cross_entropy" criterion, but this caused loading old models trained with this criterion to fail.
This diff replaces the "kd_binary_cross_entropy" criterion with the "wav2vec" criterion when loading models, and fixes this error.
It also removes the "log_keys" argument if it's `None`. Some criteria (e.g. wav2vec) require this argument to be a list, and will supply a default value of `[]` when it's absent. The presence of the `None` value prevents the use of this default value and causes an error.
Differential Revision: D28901263
fbshipit-source-id: 9b33aed35e76d2c734d1d4e2cbca1ff193a8c920
Summary:
In file_io.py, there is a logging message that happens in the global
scope. This logging message can be invoked before calling
logging.basicConfig() in fairseq_cli/train.py resulting in that
call becoming a no-op. This was causing the loglevel to remain at
WARNING.
Fix is to call logging.basicConfig() before import-ing any fairseq
libraries that may do logging in global scope.
Verified that I logging.info messages are now visible after applying
this PR.
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ ] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [ ] Did you write any new necessary tests?
## What does this PR do?
Fixes # (issue).
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3592
Reviewed By: sujitoc
Differential Revision: D28900871
Pulled By: msbaines
fbshipit-source-id: ff5393aa7c5e4cbec168ff0b846da048de76cdbc
Summary:
This diff implements teacher-student learning for TALNet.
Three classes take part in the teacher-student learning:
* The task loads the teacher models;
* The model generates predictions using the teacher models, and mixes them with the original targets;
* The `Wav2VecCriterion` reads the mixed targets to compute the loss. However, it still uses the original targets to compute the MAP and MAUC metrics.
There are two types of teachers:
* Static teachers: a file that stores predictions on training data which have been produced by running a model offline;
* Dynamic teachers: model files that are loaded at the beginning of training and executed on the fly to produce predictions.
We actually no longer use static teachers. The code about static teachers are copied over from the `KnowledgeDistillationBinaryCrossEntropyCriterion` class. This class will be cleaned up in D28728718.
The teacher models are stored in the task object, and will not be saved into checkpoints.
Reviewed By: alexeib
Differential Revision: D28728707
fbshipit-source-id: 0fcfc00db2e7194a6f7ee687cad9fa72e82a028b
Summary:
`Wav2VecCriterion` uses a sample_size for two purposes:
1. It weights the extra loss by multiplying it by sample_size;
2. It divides the total loss by sample_size before reporting them in the learning curves.
By default, when using the binary cross-entropy loss (`infonce = False`), `Wav2VecCriterion` uses the number of 1's in the label matrix as sample_size.
For TALNet, because each recording may have multiple labels, this sample_size is not a constant across batches.
TALNet also uses a consistency loss between the predictions on two different copies of augmented data as an extra loss, and it is undesirable for the weight of the extra loss to vary from batch to batch.
This diff adds a field "sample_size" to the batch in the `AcousticEventCollater`, and makes it equal to the batch size (number or recordings in a batch).
Because the extra loss is multiplied by sample_size in `Wav2VecCriterion`, this diff also divides the consistency loss by the batch size in the `forward` method of `TALNetModel`.
This diff also adds a unit test for the consistency loss.
Reviewed By: alexeib
Differential Revision: D28728699
fbshipit-source-id: dda1f2a1b02e49b894842c8990218b5fe92d0330
Summary:
Previously on cuda, LayerNorm would always default to FusedLayerNorm, which could not be exported.
Add export flag, so torch.nn.LayerNorm would be used.
Reviewed By: myleott, mikekgfb, kpuatfb
Differential Revision: D28858633
fbshipit-source-id: 58dd4945f596b2bcc94a6b74356bd9fd3c73ca1a
Summary:
for whatever reason, checkpoints are failing to save because choiceenum can't be pickled again (could be env specific). this should permanently resolve it by converting choice enum to string in the config before saving
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1915
Reviewed By: arbabu123
Differential Revision: D28784506
Pulled By: alexeib
fbshipit-source-id: 17843cfa00e8e624eb06262df8e1b71b062a237b
Summary: `ManifoldPathHandler` is automatically registered with `IOPathManager` upon importing the latter (see D27960781). Therefore it is no longer necessary to register `ManifoldPathManager` in fairseq, as introduced by D27809504 (3a90a859d4).
Reviewed By: sujitoc
Differential Revision: D28735316
fbshipit-source-id: 03e246dd17ba9f2a9a81dd4e741cce88f26feedd
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [x] Did you make sure to update the docs?
- [x] Did you write any new necessary tests?
## What does this PR do?
Fixes the use of `prefix_allowed_tokens_fn` in generation. It was working for `fairseq==0.9.0` (see https://github.com/facebookresearch/GENRE) but with the current version is broken.
## PR review
Anyone in the community is free to review the PR once the tests have passed.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3276
Reviewed By: alexeib
Differential Revision: D26725494
Pulled By: myleott
fbshipit-source-id: ce3da725f36352687e5cb5d62a59b4c89ce0b0bc
Summary:
previously hydra/infer.py did not always work for several reasons which are addressed here
new example usage:
PYTHONPATH=. python examples/speech_recognition/new/infer.py --config-dir examples/speech_recognition/hydra/conf --config-name infer task=audio_pretraining task.data=/path/to/data task.labels=ltr decoding.type=kenlm decoding.lexicon=/path/to/lexicon decoding.lmpath=/path/to/lm dataset.gen_subset=dev_other common_eval.path=/path/to/model.pt decoding.beam=5 decoding.lmweight=2 decoding.wordscore=-1
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1903
Reviewed By: arbabu123
Differential Revision: D28700795
Pulled By: alexeib
fbshipit-source-id: 66fe454de49c1bf511b3529ac683f1c8cb08e579
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [x] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ ] Did you make sure to update the docs?
- [x] Did you write any new necessary tests?
## What does this PR do?
Fixes https://github.com/pytorch/fairseq/issues/3282
Add support for `torch.cuda.amp`
AMP can be enabled by `--amp`, instead of using `--fp16` for the already present full fp16 support.
## PR review
Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3460
Reviewed By: sshleifer, msbaines
Differential Revision: D27932253
Pulled By: myleott
fbshipit-source-id: 21637aefb5e788c59bf4f3c5de6c4a80f7319543
Summary:
using the very useful feature added by QuentinDuval https://github.com/facebookresearch/fairscale/pull/683/files , we can consolidate sharded states into a full regular states. this allows inferences on sharded state almost transparently.
The main complexity comes from trying to be smart about what kind of checkpoint the user wants to load. not sure if this is over-engineering
1. if the file checkpoint-shard0.pt exists, and `--checkpoint-shard-count` is > 1, then we load sharded FSDP checkpoint
2. if checkpoint-shard0.pt exists but --checkpoint-shard-count=1, we load consolidated FSDP checkpoint
3. if checkpoint-shard0.pt does not exist, but --checkpoint-shard-count > 1, we load model parallel checkpoint
4. otherwise we are loading a single, plain checkpoint.
In theory we could be even smarter and load shard0.pt to check how many more checkpoints are needed. this is not implemented, though it will save the user having to specify --checkpoint-shard-count.
Reviewed By: sshleifer
Differential Revision: D28563441
fbshipit-source-id: dcafcaa7c9eaf5c9ff94f55c16bb3424c98dfa59
Summary:
## What does this PR do?
Updated the models' table in README to show the model sizes and groups pretrained models followed by fine tuned models.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1901
Reviewed By: wnhsu
Differential Revision: D28688952
Pulled By: hikushalhere
fbshipit-source-id: 8621398a785caa3d7bdc68367789ad7f48499d0d
Summary:
previous changes broke saving updating w2v_args in config as the model had a copy of the config. this change makes the task copy over the field to save. not the nicest approach, but it works for now
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1896
Reviewed By: arbabu123
Differential Revision: D28658802
Pulled By: alexeib
fbshipit-source-id: a13866c42c3b88c48b8b91864c1bf1aeaeba4e8a
Summary:
Old logs:
```
/arrow/cpp/src/plasma/store.cc:1274: Allowing the Plasma store to use up to 107.374GB of memory.
```
New logs:
```
... up to 1e-05GB of memory.
```
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1893
Reviewed By: myleott
Differential Revision: D28641488
Pulled By: sshleifer
fbshipit-source-id: 3373526042cdcbf434c61790be62a09f15e6ad06
Summary:
## What does this PR do?
This PR updates some outdated code from the Hugging Face Transformers library to the new, better format.
## PR review
alexeib
## Did you have fun?
Make sure you had fun coding �
Pull Request resolved: https://github.com/pytorch/fairseq/pull/3502
Reviewed By: arbabu123
Differential Revision: D28140574
Pulled By: alexeib
fbshipit-source-id: f03643e7ebba04015d942a3aa9529f7f6600c734
Summary:
- fixing the default value for `encoder_freezing_updates` in s2t transformer
- fixing N-best checkpoint saving: the previous implementation compares the new checkpoint with only the previous best one but not the previous N best ones. This leads to suboptimal results on N-best checkpoint averaging.
Reviewed By: jmp84
Differential Revision: D28546493
fbshipit-source-id: 44ec6d5ab49347f392d71269c5dcfd154b00c11e
Summary:
# Before submitting
- [ ] Was this discussed/approved via a Github issue? (no need for typos, doc improvements)
- [ X] Did you read the [contributor guideline](https://github.com/pytorch/fairseq/blob/master/CONTRIBUTING.md)?
- [ X] Did you make sure to update the docs?
- [ X] Did you write any new necessary tests?
## What does this PR do?
This PR adds relevant code for pre-training HuBERT and fine-tuning a pretrained HuBERT for ASR. It also shared trained models of different sizes.
Pull Request resolved: https://github.com/fairinternal/fairseq-py/pull/1877
Reviewed By: wnhsu
Differential Revision: D28513359
Pulled By: hikushalhere
fbshipit-source-id: 8755862f236b7d840105b0fa8f5461ac053d79cc
Summary:
overall just wondering if feature is desirable. if it is, the next diff which supports loading sharded checkpoint into a consolidated state dict cleaner.
a couple advantages
1. allows resuming from other DDP trainers.
2. allows resuming into other DDP trainers. or FSDP of a different configuration.
3. none-sharded FSDP checkpoint can be loaded with regular load_model_ensemble_and_task()
For old training workflow that's not using `--use-sharded-state`, please rename the checkpoint to remove the "-shard0" for resuming training.
Reviewed By: sshleifer
Differential Revision: D28563032
fbshipit-source-id: ced72bed969319ab6306059721f56e29b2c3d892
