Add linting with black (#2678)

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/main/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/2678

Reviewed By: Mortimerp9

Differential Revision: D32653381

Pulled By: dianaml0

fbshipit-source-id: 2810d14867cd7d64f4d340740e2b590b82de47fe

.github/workflows/build.yml

@@ -53,3 +53,8 @@ jobs:
     - name: Run tests
       run: |
           python setup.py test
+
+    - name: Lint with black
+      run: |
+        pip install black
+        black --check . --extend-exclude 'examples|fairseq\/model_parallel\/megatron'

fairseq/__init__.py

@@ -27,6 +27,7 @@ sys.modules["fairseq.progress_bar"] = progress_bar
 
 # initialize hydra
 from fairseq.dataclass.initialize import hydra_init
+
 hydra_init()
 
 import fairseq.criterions  # noqa

fairseq/benchmark/dummy_mt.py

@@ -7,10 +7,10 @@ import logging
 
 import numpy as np
 import torch
+
 from fairseq.data import Dictionary, FairseqDataset
 from fairseq.tasks import LegacyFairseqTask, register_task
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -36,7 +36,7 @@ class DummyMTTask(LegacyFairseqTask):
 
     @classmethod
     def setup_task(cls, args, **kwargs):
-        """Setup the task. """
+        """Setup the task."""
         dictionary = Dictionary()
         for i in range(args.dict_size):
             dictionary.add_symbol("word{}".format(i))

fairseq/checkpoint_utils.py

@@ -96,10 +96,7 @@ def save_checkpoint(cfg: CheckpointConfig, trainer, epoch_itr, val_loss):
 
         checkpoint_conds[
             "checkpoint.best_{}_{:.3f}{}{}.pt".format(
-                cfg.best_checkpoint_metric,
-                val_loss,
-                rand_sfx,
-                suffix
+                cfg.best_checkpoint_metric, val_loss, rand_sfx, suffix
             )
         ] = worst_best is None or is_better(val_loss, worst_best)
     checkpoint_conds[
@@ -468,9 +465,7 @@ def load_model_ensemble_and_task(
                     and len(state["optimizer_history"]) > 0
                     and "num_updates" in state["optimizer_history"][-1]
                 ):
-                    model.set_num_updates(
-                        state["optimizer_history"][-1]["num_updates"]
-                    )
+                    model.set_num_updates(state["optimizer_history"][-1]["num_updates"])
                 model.load_state_dict(
                     state["model"], strict=strict, model_cfg=cfg.model
                 )
@@ -588,9 +583,8 @@ def _upgrade_state_dict(state):
     # backward compatibility, cfg updates
     if "args" in state and state["args"] is not None:
         # old model checkpoints may not have separate source/target positions
-        if (
-            hasattr(state["args"], "max_positions")
-            and not hasattr(state["args"], "max_source_positions")
+        if hasattr(state["args"], "max_positions") and not hasattr(
+            state["args"], "max_source_positions"
         ):
             state["args"].max_source_positions = state["args"].max_positions
             state["args"].max_target_positions = state["args"].max_positions
@@ -615,13 +609,10 @@ def _upgrade_state_dict(state):
             state["args"].stop_min_lr = state["args"].min_lr
             del state["args"].min_lr
         # binary_cross_entropy / kd_binary_cross_entropy => wav2vec criterion
-        if (
-            hasattr(state["args"], "criterion")
-            and state["args"].criterion in [
-                "binary_cross_entropy",
-                "kd_binary_cross_entropy",
-            ]
-        ):
+        if hasattr(state["args"], "criterion") and state["args"].criterion in [
+            "binary_cross_entropy",
+            "kd_binary_cross_entropy",
+        ]:
             state["args"].criterion = "wav2vec"
         # remove log_keys if it's None (criteria will supply a default value of [])
         if hasattr(state["args"], "log_keys") and state["args"].log_keys is None:
@@ -659,7 +650,9 @@ def _upgrade_state_dict(state):
             ):
                 cfg.task.eval_wer_config.print_alignment = "hard"
             if "generation" in cfg and isinstance(cfg.generation.print_alignment, bool):
-                cfg.generation.print_alignment = "hard" if cfg.generation.print_alignment else None
+                cfg.generation.print_alignment = (
+                    "hard" if cfg.generation.print_alignment else None
+                )
             if (
                 "model" in cfg
                 and "w2v_args" in cfg.model
@@ -833,16 +826,16 @@ def load_ema_from_checkpoint(fpath):
     params_dict = collections.OrderedDict()
     new_state = None
 
-    with PathManager.open(fpath, 'rb') as f:
+    with PathManager.open(fpath, "rb") as f:
         new_state = torch.load(
             f,
             map_location=(
-                lambda s, _: torch.serialization.default_restore_location(s, 'cpu')
+                lambda s, _: torch.serialization.default_restore_location(s, "cpu")
             ),
         )
 
         # EMA model is stored in a separate "extra state"
-        model_params = new_state['extra_state']['ema']
+        model_params = new_state["extra_state"]["ema"]
 
         for key in list(model_params.keys()):
             p = model_params[key]
@@ -860,5 +853,5 @@ def load_ema_from_checkpoint(fpath):
                 "ema model weights, is this model trained with EMA?"
             )
 
-    new_state['model'] = params_dict
+    new_state["model"] = params_dict
     return new_state

fairseq/criterions/fastspeech2_loss.py

@@ -20,9 +20,7 @@ from fairseq.models.fairseq_model import FairseqEncoderModel
 
 @dataclass
 class FastSpeech2CriterionConfig(FairseqDataclass):
-    ctc_weight: float = field(
-        default=0.0, metadata={"help": "weight for CTC loss"}
-    )
+    ctc_weight: float = field(default=0.0, metadata={"help": "weight for CTC loss"})
 
 
 @register_criterion("fastspeech2", dataclass=FastSpeech2CriterionConfig)
@@ -44,7 +42,7 @@ class FastSpeech2Loss(FairseqCriterion):
             speaker=sample["speaker"],
             durations=sample["durations"],
             pitches=sample["pitches"],
-            energies=sample["energies"]
+            energies=sample["energies"],
         )
 
         src_mask = lengths_to_mask(sample["net_input"]["src_lengths"])
@@ -57,8 +55,7 @@ class FastSpeech2Loss(FairseqCriterion):
         feat_out, feat = _feat_out[tgt_mask], sample["target"][tgt_mask]
         l1_loss = F.l1_loss(feat_out, feat, reduction=reduction)
         if _feat_out_post is not None:
-            l1_loss += F.l1_loss(_feat_out_post[tgt_mask], feat,
-                                 reduction=reduction)
+            l1_loss += F.l1_loss(_feat_out_post[tgt_mask], feat, reduction=reduction)
 
         pitch_loss = F.mse_loss(pitch_out, pitches, reduction=reduction)
         energy_loss = F.mse_loss(energy_out, energies, reduction=reduction)
@@ -69,16 +66,23 @@ class FastSpeech2Loss(FairseqCriterion):
         log_dur = torch.log(dur + 1)[src_mask]
         dur_loss = F.mse_loss(log_dur_out, log_dur, reduction=reduction)
 
-        ctc_loss = torch.tensor(0.).type_as(l1_loss)
-        if self.ctc_weight > 0.:
+        ctc_loss = torch.tensor(0.0).type_as(l1_loss)
+        if self.ctc_weight > 0.0:
             lprobs = model.get_normalized_probs((_feat_out,), log_probs=True)
             lprobs = lprobs.transpose(0, 1)  # T x B x C
             src_mask = lengths_to_mask(src_lens)
             src_tokens_flat = src_tokens.masked_select(src_mask)
-            ctc_loss = F.ctc_loss(
-                lprobs, src_tokens_flat, tgt_lens, src_lens,
-                reduction=reduction, zero_infinity=True
-            ) * self.ctc_weight
+            ctc_loss = (
+                F.ctc_loss(
+                    lprobs,
+                    src_tokens_flat,
+                    tgt_lens,
+                    src_lens,
+                    reduction=reduction,
+                    zero_infinity=True,
+                )
+                * self.ctc_weight
+            )
 
         loss = l1_loss + dur_loss + pitch_loss + energy_loss + ctc_loss
 
@@ -102,8 +106,12 @@ class FastSpeech2Loss(FairseqCriterion):
         ntot = sum(ns)
         ws = [n / (ntot + 1e-8) for n in ns]
         for key in [
-            "loss", "l1_loss", "dur_loss", "pitch_loss", "energy_loss",
-            "ctc_loss"
+            "loss",
+            "l1_loss",
+            "dur_loss",
+            "pitch_loss",
+            "energy_loss",
+            "ctc_loss",
         ]:
             vals = [log.get(key, 0) for log in logging_outputs]
             val = sum(val * w for val, w in zip(vals, ws))
@@ -115,10 +123,10 @@ class FastSpeech2Loss(FairseqCriterion):
             return
         n = sum(log.get("targ_frames", 0) for log in logging_outputs)
         for key, new_key in [
-                ("mcd_loss", "mcd_loss"),
-                ("pred_frames", "pred_ratio"),
-                ("nins", "ins_rate"),
-                ("ndel", "del_rate"),
+            ("mcd_loss", "mcd_loss"),
+            ("pred_frames", "pred_ratio"),
+            ("nins", "ins_rate"),
+            ("ndel", "del_rate"),
         ]:
             val = sum(log.get(key, 0) for log in logging_outputs)
             metrics.log_scalar(new_key, val / n, n, round=3)

fairseq/criterions/hubert_criterion.py

@@ -37,7 +37,14 @@ class HubertCriterionConfig(FairseqDataclass):
 
 @register_criterion("hubert", dataclass=HubertCriterionConfig)
 class HubertCriterion(FairseqCriterion):
-    def __init__(self, task, pred_masked_weight, pred_nomask_weight, loss_weights=None, log_keys=None):
+    def __init__(
+        self,
+        task,
+        pred_masked_weight,
+        pred_nomask_weight,
+        loss_weights=None,
+        log_keys=None,
+    ):
         super().__init__(task)
         self.pred_masked_weight = pred_masked_weight
         self.pred_nomask_weight = pred_nomask_weight
@@ -52,7 +59,7 @@ class HubertCriterion(FairseqCriterion):
         3) logging outputs to display while training
         """
         net_output = model(target_list=sample["target_list"], **sample["net_input"])
-        loss = 0.
+        loss = 0.0
         sample_size = 0
         logging_output = {}
         reduction = "sum" if reduce else "none"
@@ -89,7 +96,9 @@ class HubertCriterion(FairseqCriterion):
                 names = [names]
             if len(self.loss_weights) == 1 and len(extra_losses) != 1:
                 self.loss_weights = [self.loss_weights[0]] * len(extra_losses)
-            assert len(extra_losses) == len(self.loss_weights), f"{len(extra_losses)}, {len(self.loss_weights)}"
+            assert len(extra_losses) == len(
+                self.loss_weights
+            ), f"{len(extra_losses)}, {len(self.loss_weights)}"
             for p, n, coef in zip(extra_losses, names, self.loss_weights):
                 if coef != 0 and p is not None:
                     p = coef * p.float() * sample_size
@@ -140,12 +149,20 @@ class HubertCriterion(FairseqCriterion):
         ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
         sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
 
-        metrics.log_scalar("loss", loss_sum / sample_size / math.log(2), sample_size, round=3)
+        metrics.log_scalar(
+            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
+        )
         if sample_size != ntokens:
-            metrics.log_scalar("nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3)
-            metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg))
+            metrics.log_scalar(
+                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
+            )
+            metrics.log_derived(
+                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
+            )
         else:
-            metrics.log_derived("ppl", lambda meters: utils.get_perplexity(meters["loss"].avg))
+            metrics.log_derived(
+                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
+            )
 
         counts = {}
         for lk in logging_outputs[0].keys():

fairseq/criterions/label_smoothed_cross_entropy_latency_augmented.py

@@ -9,19 +9,20 @@ from fairseq import metrics, utils
 from fairseq.criterions import register_criterion
 from fairseq.criterions.label_smoothed_cross_entropy import (
     LabelSmoothedCrossEntropyCriterion,
-    LabelSmoothedCrossEntropyCriterionConfig
+    LabelSmoothedCrossEntropyCriterionConfig,
 )
 
 try:
     from simuleval.metrics.latency import (
         AverageLagging,
         AverageProportion,
-        DifferentiableAverageLagging
+        DifferentiableAverageLagging,
     )
+
     LATENCY_METRICS = {
         "average_lagging": AverageLagging,
         "average_proportion": AverageProportion,
-        "differentiable_average_lagging":  DifferentiableAverageLagging,
+        "differentiable_average_lagging": DifferentiableAverageLagging,
     }
 except ImportError:
     LATENCY_METRICS = None
@@ -56,9 +57,10 @@ class LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig(
         metadata={"help": "Add latency loss after certain steps"},
     )
 
+
 @register_criterion(
     "latency_augmented_label_smoothed_cross_entropy",
-    dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig
+    dataclass=LabelSmoothedCrossEntropyCriterionLatencyAugmentConfig,
 )
 class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
     LabelSmoothedCrossEntropyCriterion
@@ -101,9 +103,9 @@ class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
 
         if self.latency_update_after > 0:
             num_updates = getattr(model.decoder, "num_updates", None)
-            assert num_updates is not None, (
-                "model.decoder doesn't have attribute 'num_updates'"
-            )
+            assert (
+                num_updates is not None
+            ), "model.decoder doesn't have attribute 'num_updates'"
             if num_updates <= self.latency_update_after:
                 latency_loss = 0
 
@@ -134,9 +136,7 @@ class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
         assert (
             net_output[-1].encoder_padding_mask is None
             or not net_output[-1].encoder_padding_mask[:, 0].any()
-        ), (
-            "Only right padding on source is supported."
-        )
+        ), "Only right padding on source is supported."
         # 1. Obtain the expected alignment
         alpha_list = [item["alpha"] for item in net_output[1].attn_list]
         num_layers = len(alpha_list)
@@ -174,8 +174,7 @@ class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
             .view(-1)
         )
         expected_latency = LATENCY_METRICS[self.latency_avg_type](
-            expected_delays, src_lengths, None,
-            target_padding_mask=target_padding_mask
+            expected_delays, src_lengths, None, target_padding_mask=target_padding_mask
         )
 
         # 2.1 average expected latency of heads
@@ -210,24 +209,12 @@ class LatencyAugmentedLabelSmoothedCrossEntropyCriterion(
     @classmethod
     def reduce_metrics(cls, logging_outputs) -> None:
         super().reduce_metrics(logging_outputs)
-        latency = sum(
-            log.get("latency", 0) for log in logging_outputs
-        )
-        delays_var = sum(
-            log.get("delays_var", 0) for log in logging_outputs
-        )
-        latency_loss = sum(
-            log.get("latency_loss", 0) for log in logging_outputs
-        )
+        latency = sum(log.get("latency", 0) for log in logging_outputs)
+        delays_var = sum(log.get("delays_var", 0) for log in logging_outputs)
+        latency_loss = sum(log.get("latency_loss", 0) for log in logging_outputs)
         nsentences = sum(log.get("nsentences", 0) for log in logging_outputs)
+        metrics.log_scalar("latency", latency.float() / nsentences, nsentences, round=3)
+        metrics.log_scalar("delays_var", delays_var / nsentences, nsentences, round=3)
         metrics.log_scalar(
-            "latency", latency.float() / nsentences, nsentences, round=3
-        )
-        metrics.log_scalar(
-            "delays_var", delays_var / nsentences,
-            nsentences, round=3
-        )
-        metrics.log_scalar(
-            "latency_loss", latency_loss / nsentences,
-            nsentences, round=3
+            "latency_loss", latency_loss / nsentences, nsentences, round=3
         )

fairseq/criterions/tacotron2_loss.py

@@ -41,9 +41,7 @@ class Tacotron2CriterionConfig(FairseqDataclass):
         default=0.4,
         metadata={"help": "weight of positive examples for BCE loss"},
     )
-    ctc_weight: float = field(
-        default=0.0, metadata={"help": "weight for CTC loss"}
-    )
+    ctc_weight: float = field(default=0.0, metadata={"help": "weight for CTC loss"})
     sentence_avg: bool = II("optimization.sentence_avg")
 
 
@@ -70,8 +68,7 @@ class GuidedAttentionLoss(torch.nn.Module):
         bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens)
         weights = torch.zeros((bsz, max_t_len, max_s_len))
         for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)):
-            weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len,
-                                                          self.sigma)
+            weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len, self.sigma)
         return weights
 
     @staticmethod
@@ -90,9 +87,16 @@ class GuidedAttentionLoss(torch.nn.Module):
 
 @register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig)
 class Tacotron2Criterion(FairseqCriterion):
-    def __init__(self, task, sentence_avg, n_frames_per_step,
-                 use_guided_attention_loss, guided_attention_loss_sigma,
-                 bce_pos_weight, ctc_weight):
+    def __init__(
+        self,
+        task,
+        sentence_avg,
+        n_frames_per_step,
+        use_guided_attention_loss,
+        guided_attention_loss_sigma,
+        bce_pos_weight,
+        ctc_weight,
+    ):
         super().__init__(task)
         self.sentence_avg = sentence_avg
         self.n_frames_per_step = n_frames_per_step
@@ -120,31 +124,42 @@ class Tacotron2Criterion(FairseqCriterion):
             prev_output_tokens=sample["net_input"]["prev_output_tokens"],
             incremental_state=None,
             target_lengths=tgt_lens,
-            speaker=sample["speaker"]
+            speaker=sample["speaker"],
         )
 
         l1_loss, mse_loss, eos_loss = self.compute_loss(
-            extra["feature_out"], feat_out, eos_out, feat_tgt, eos_tgt,
-            tgt_lens, reduction,
+            extra["feature_out"],
+            feat_out,
+            eos_out,
+            feat_tgt,
+            eos_tgt,
+            tgt_lens,
+            reduction,
         )
-        attn_loss = torch.tensor(0.).type_as(l1_loss)
+        attn_loss = torch.tensor(0.0).type_as(l1_loss)
         if self.guided_attn is not None:
-            attn_loss = self.guided_attn(extra['attn'], src_lens, tgt_lens, reduction)
-        ctc_loss = torch.tensor(0.).type_as(l1_loss)
-        if self.ctc_weight > 0.:
+            attn_loss = self.guided_attn(extra["attn"], src_lens, tgt_lens, reduction)
+        ctc_loss = torch.tensor(0.0).type_as(l1_loss)
+        if self.ctc_weight > 0.0:
             net_output = (feat_out, eos_out, extra)
             lprobs = model.get_normalized_probs(net_output, log_probs=True)
             lprobs = lprobs.transpose(0, 1)  # T x B x C
             src_mask = lengths_to_mask(src_lens)
             src_tokens_flat = src_tokens.masked_select(src_mask)
-            ctc_loss = F.ctc_loss(
-                lprobs, src_tokens_flat, tgt_lens, src_lens,
-                reduction=reduction, zero_infinity=True
-            ) * self.ctc_weight
+            ctc_loss = (
+                F.ctc_loss(
+                    lprobs,
+                    src_tokens_flat,
+                    tgt_lens,
+                    src_lens,
+                    reduction=reduction,
+                    zero_infinity=True,
+                )
+                * self.ctc_weight
+            )
         loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss
 
-        sample_size = sample["nsentences"] if self.sentence_avg \
-            else sample["ntokens"]
+        sample_size = sample["nsentences"] if self.sentence_avg else sample["ntokens"]
         logging_output = {
             "loss": utils.item(loss.data),
             "ntokens": sample["ntokens"],
@@ -158,8 +173,16 @@ class Tacotron2Criterion(FairseqCriterion):
         }
         return loss, sample_size, logging_output
 
-    def compute_loss(self, feat_out, feat_out_post, eos_out, feat_tgt,
-                     eos_tgt, tgt_lens, reduction="mean"):
+    def compute_loss(
+        self,
+        feat_out,
+        feat_out_post,
+        eos_out,
+        feat_tgt,
+        eos_tgt,
+        tgt_lens,
+        reduction="mean",
+    ):
         mask = lengths_to_mask(tgt_lens)
         _eos_out = eos_out[mask].squeeze()
         _eos_tgt = eos_tgt[mask]
@@ -167,17 +190,17 @@ class Tacotron2Criterion(FairseqCriterion):
         _feat_out = feat_out[mask]
         _feat_out_post = feat_out_post[mask]
 
-        l1_loss = (
-            F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) +
-            F.l1_loss(_feat_out_post, _feat_tgt, reduction=reduction)
+        l1_loss = F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) + F.l1_loss(
+            _feat_out_post, _feat_tgt, reduction=reduction
         )
-        mse_loss = (
-            F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) +
-            F.mse_loss(_feat_out_post, _feat_tgt, reduction=reduction)
+        mse_loss = F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) + F.mse_loss(
+            _feat_out_post, _feat_tgt, reduction=reduction
         )
         eos_loss = F.binary_cross_entropy_with_logits(
-            _eos_out, _eos_tgt, pos_weight=torch.tensor(self.bce_pos_weight),
-            reduction=reduction
+            _eos_out,
+            _eos_tgt,
+            pos_weight=torch.tensor(self.bce_pos_weight),
+            reduction=reduction,
         )
         return l1_loss, mse_loss, eos_loss
 
@@ -197,10 +220,10 @@ class Tacotron2Criterion(FairseqCriterion):
             return
         n = sum(log.get("targ_frames", 0) for log in logging_outputs)
         for key, new_key in [
-                ("mcd_loss", "mcd_loss"),
-                ("pred_frames", "pred_ratio"),
-                ("nins", "ins_rate"),
-                ("ndel", "del_rate"),
+            ("mcd_loss", "mcd_loss"),
+            ("pred_frames", "pred_ratio"),
+            ("nins", "ins_rate"),
+            ("ndel", "del_rate"),
         ]:
             val = sum(log.get(key, 0) for log in logging_outputs)
             metrics.log_scalar(new_key, val / n, n, round=3)

fairseq/criterions/wav2vec_criterion.py

@@ -33,6 +33,7 @@ class Wav2VecCriterionConfig(FairseqDataclass):
         metadata={"help": "output keys to log"},
     )
 
+
 @register_criterion("wav2vec", dataclass=Wav2VecCriterionConfig)
 class Wav2vecCriterion(FairseqCriterion):
     def __init__(self, task, infonce=False, loss_weights=None, log_keys=None):
@@ -76,16 +77,16 @@ class Wav2vecCriterion(FairseqCriterion):
             # we don't shrink tensors using mask_indices.
             # Instead, we use mask indices to adjust loss.
             mi = (
-                sample['net_input']['mask_indices']
+                sample["net_input"]["mask_indices"]
                 .transpose(0, 1)  # logits are transposed in `model.get_logits`
                 .reshape(logits.size(0))
             )
             loss = (loss * mi).sum() if reduce else (loss * mi)
 
-        if 'sample_size' in sample:
-            sample_size = sample['sample_size']
-        elif 'mask_indices' in sample['net_input']:
-            sample_size = sample['net_input']['mask_indices'].sum()
+        if "sample_size" in sample:
+            sample_size = sample["sample_size"]
+        elif "mask_indices" in sample["net_input"]:
+            sample_size = sample["net_input"]["mask_indices"].sum()
         else:
             sample_size = target.numel() if self.infonce else target.long().sum().item()
         losses.append(loss.detach().clone())
@@ -216,8 +217,8 @@ class Wav2vecCriterion(FairseqCriterion):
                     metrics.log_scalar(k, val / len(logging_outputs), round=3)
 
     # FIXME: revert when gather based xla reduction is implemented
-    #@staticmethod
-    #def logging_outputs_can_be_summed() -> bool:
+    # @staticmethod
+    # def logging_outputs_can_be_summed() -> bool:
     def logging_outputs_can_be_summed(self) -> bool:
         """
         Whether the logging outputs returned by `forward` can be summed

fairseq/data/add_target_dataset.py

@@ -20,7 +20,7 @@ class AddTargetDataset(BaseWrapperDataset):
         process_label=None,
         label_len_fn=None,
         add_to_input=False,
-        text_compression_level=TextCompressionLevel.none
+        text_compression_level=TextCompressionLevel.none,
     ):
         super().__init__(dataset)
         self.labels = labels

fairseq/data/audio/audio_utils.py

@@ -18,26 +18,28 @@ FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS = {".npy", ".wav", ".flac", ".ogg"}
 
 
 def convert_waveform(
-        waveform: Union[np.ndarray, torch.Tensor], sample_rate: int,
-        normalize_volume: bool = False, to_mono: bool = False,
-        to_sample_rate: Optional[int] = None
+    waveform: Union[np.ndarray, torch.Tensor],
+    sample_rate: int,
+    normalize_volume: bool = False,
+    to_mono: bool = False,
+    to_sample_rate: Optional[int] = None,
 ) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
     """convert a waveform:
-        - to a target sample rate
-        - from multi-channel to mono channel
-        - volume normalization
+    - to a target sample rate
+    - from multi-channel to mono channel
+    - volume normalization
 
-        Args:
-            waveform (numpy.ndarray or torch.Tensor): 2D original waveform
-                (channels x length)
-            sample_rate (int): original sample rate
-            normalize_volume (bool): perform volume normalization
-            to_mono (bool): convert to mono channel if having multiple channels
-            to_sample_rate (Optional[int]): target sample rate
-        Returns:
-            waveform (numpy.ndarray): converted 2D waveform (channels x length)
-            sample_rate (float): target sample rate
-        """
+    Args:
+        waveform (numpy.ndarray or torch.Tensor): 2D original waveform
+            (channels x length)
+        sample_rate (int): original sample rate
+        normalize_volume (bool): perform volume normalization
+        to_mono (bool): convert to mono channel if having multiple channels
+        to_sample_rate (Optional[int]): target sample rate
+    Returns:
+        waveform (numpy.ndarray): converted 2D waveform (channels x length)
+        sample_rate (float): target sample rate
+    """
     try:
         import torchaudio.sox_effects as ta_sox
     except ImportError:
@@ -63,10 +65,14 @@ def convert_waveform(
 
 
 def get_waveform(
-        path_or_fp: Union[str, BinaryIO], normalization: bool = True,
-        mono: bool = True, frames: int = -1, start: int = 0,
-        always_2d: bool = True, output_sample_rate: Optional[int] = None,
-        normalize_volume: bool = False
+    path_or_fp: Union[str, BinaryIO],
+    normalization: bool = True,
+    mono: bool = True,
+    frames: int = -1,
+    start: int = 0,
+    always_2d: bool = True,
+    output_sample_rate: Optional[int] = None,
+    normalize_volume: bool = False,
 ) -> Tuple[np.ndarray, int]:
     """Get the waveform and sample rate of a 16-bit WAV/FLAC/OGG Vorbis audio.
 
@@ -98,8 +104,11 @@ def get_waveform(
     )
     waveform = waveform.T  # T x C -> C x T
     waveform, sample_rate = convert_waveform(
-        waveform, sample_rate, normalize_volume=normalize_volume, to_mono=mono,
-        to_sample_rate=output_sample_rate
+        waveform,
+        sample_rate,
+        normalize_volume=normalize_volume,
+        to_mono=mono,
+        to_sample_rate=output_sample_rate,
     )
 
     if not normalization:
@@ -182,7 +191,7 @@ def is_sf_audio_data(data: bytes) -> bool:
 def mmap_read(path: str, offset: int, length: int) -> bytes:
     with open(path, "rb") as f:
         with mmap.mmap(f.fileno(), length=0, access=mmap.ACCESS_READ) as mmap_o:
-            data = mmap_o[offset: offset + length]
+            data = mmap_o[offset : offset + length]
     return data
 
 
@@ -215,9 +224,7 @@ def parse_path(path: str) -> Tuple[str, List[int]]:
     return _path, slice_ptr
 
 
-def get_window(
-        window_fn: callable, n_fft: int, win_length: int
-) -> torch.Tensor:
+def get_window(window_fn: callable, n_fft: int, win_length: int) -> torch.Tensor:
     padding = n_fft - win_length
     assert padding >= 0
     return F.pad(window_fn(win_length), (padding // 2, padding - padding // 2))
@@ -226,13 +233,13 @@ def get_window(
 def get_fourier_basis(n_fft: int) -> torch.Tensor:
     basis = np.fft.fft(np.eye(n_fft))
     basis = np.vstack(
-        [np.real(basis[:n_fft // 2 + 1, :]), np.imag(basis[:n_fft // 2 + 1, :])]
+        [np.real(basis[: n_fft // 2 + 1, :]), np.imag(basis[: n_fft // 2 + 1, :])]
     )
     return torch.from_numpy(basis).float()
 
 
 def get_mel_filters(
-        sample_rate: int, n_fft: int, n_mels: int, f_min: float, f_max: float
+    sample_rate: int, n_fft: int, n_mels: int, f_min: float, f_max: float
 ) -> torch.Tensor:
     try:
         import librosa
@@ -244,8 +251,12 @@ def get_mel_filters(
 
 class TTSSpectrogram(torch.nn.Module):
     def __init__(
-            self, n_fft: int, win_length: int, hop_length: int,
-            window_fn: callable = torch.hann_window, return_phase: bool = False
+        self,
+        n_fft: int,
+        win_length: int,
+        hop_length: int,
+        window_fn: callable = torch.hann_window,
+        return_phase: bool = False,
     ) -> None:
         super(TTSSpectrogram, self).__init__()
         self.n_fft = n_fft
@@ -254,16 +265,16 @@ class TTSSpectrogram(torch.nn.Module):
 
         basis = get_fourier_basis(n_fft).unsqueeze(1)
         basis *= get_window(window_fn, n_fft, win_length)
-        self.register_buffer('basis', basis)
+        self.register_buffer("basis", basis)
 
     def forward(
-            self, waveform: torch.Tensor
+        self, waveform: torch.Tensor
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         padding = (self.n_fft // 2, self.n_fft // 2)
-        x = F.pad(waveform.unsqueeze(1), padding, mode='reflect')
+        x = F.pad(waveform.unsqueeze(1), padding, mode="reflect")
         x = F.conv1d(x, self.basis, stride=self.hop_length)
-        real_part = x[:, :self.n_fft // 2 + 1, :]
-        imag_part = x[:, self.n_fft // 2 + 1:, :]
+        real_part = x[:, : self.n_fft // 2 + 1, :]
+        imag_part = x[:, self.n_fft // 2 + 1 :, :]
         magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
         if self.return_phase:
             phase = torch.atan2(imag_part, real_part)
@@ -273,13 +284,11 @@ class TTSSpectrogram(torch.nn.Module):
 
 class TTSMelScale(torch.nn.Module):
     def __init__(
-            self, n_mels: int, sample_rate: int, f_min: float, f_max: float,
-            n_stft: int
+        self, n_mels: int, sample_rate: int, f_min: float, f_max: float, n_stft: int
     ) -> None:
         super(TTSMelScale, self).__init__()
-        basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min,
-                                f_max)
-        self.register_buffer('basis', basis)
+        basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max)
+        self.register_buffer("basis", basis)
 
     def forward(self, specgram: torch.Tensor) -> torch.Tensor:
         return torch.matmul(self.basis, specgram)

fairseq/data/audio/frm_text_to_speech_dataset.py

@@ -13,11 +13,10 @@ from typing import List, Optional
 import numpy as np
 import torch
 from fairseq.data import Dictionary
-from fairseq.data.audio.speech_to_text_dataset import (
-    S2TDataConfig
-)
+from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
 from fairseq.data.audio.text_to_speech_dataset import (
-    TextToSpeechDataset, TextToSpeechDatasetCreator
+    TextToSpeechDataset,
+    TextToSpeechDatasetCreator,
 )
 
 logger = logging.getLogger(__name__)
@@ -48,7 +47,7 @@ class FrmTextToSpeechDataset(TextToSpeechDataset):
         chunk_incr=5,
         add_eos=True,
         dedup=True,
-        ref_fpu=-1
+        ref_fpu=-1,
     ):
         # It assumes texts are encoded at a fixed frame-rate
         super().__init__(
@@ -67,7 +66,7 @@ class FrmTextToSpeechDataset(TextToSpeechDataset):
             pre_tokenizer=pre_tokenizer,
             bpe_tokenizer=bpe_tokenizer,
             n_frames_per_step=n_frames_per_step,
-            speaker_to_id=speaker_to_id
+            speaker_to_id=speaker_to_id,
         )
 
         self.do_chunk = do_chunk
@@ -92,24 +91,23 @@ class FrmTextToSpeechDataset(TextToSpeechDataset):
         fpu = source.size(0) / target.size(0)  # frame-per-unit
         fps = self.n_frames_per_step
         assert (
-            self.ref_fpu == -1 or
-            abs((fpu * fps - self.ref_fpu) / self.ref_fpu) < 0.1
+            self.ref_fpu == -1 or abs((fpu * fps - self.ref_fpu) / self.ref_fpu) < 0.1
         ), f"{fpu*fps} != {self.ref_fpu}"
 
         # only chunk training split
         if self.is_train_split and self.do_chunk and self.chunk_size > 0:
-            lang = target[:int(self.data_cfg.prepend_tgt_lang_tag)]
-            text = target[int(self.data_cfg.prepend_tgt_lang_tag):]
+            lang = target[: int(self.data_cfg.prepend_tgt_lang_tag)]
+            text = target[int(self.data_cfg.prepend_tgt_lang_tag) :]
             size = len(text)
             chunk_size = min(self.chunk_size, size)
             chunk_start = np.random.randint(size - chunk_size + 1)
-            text = text[chunk_start:chunk_start+chunk_size]
+            text = text[chunk_start : chunk_start + chunk_size]
             target = torch.cat((lang, text), 0)
 
             f_size = int(np.floor(chunk_size * fpu))
             f_start = int(np.floor(chunk_start * fpu))
-            assert(f_size > 0)
-            source = source[f_start:f_start+f_size, :]
+            assert f_size > 0
+            source = source[f_start : f_start + f_size, :]
 
         if self.dedup:
             target = torch.unique_consecutive(target)
@@ -126,10 +124,12 @@ class FrmTextToSpeechDataset(TextToSpeechDataset):
             self.chunk_size = self.chunk_init + epoch * self.chunk_incr
             if self.chunk_bound > 0:
                 self.chunk_size = min(self.chunk_size, self.chunk_bound)
-            logger.info((
-                f"{self.split}: setting chunk size "
-                f"from {old} to {self.chunk_size}"
-            ))
+            logger.info(
+                (
+                    f"{self.split}: setting chunk size "
+                    f"from {old} to {self.chunk_size}"
+                )
+            )
 
 
 class FrmTextToSpeechDatasetCreator(TextToSpeechDatasetCreator):
@@ -152,7 +152,7 @@ class FrmTextToSpeechDatasetCreator(TextToSpeechDatasetCreator):
         chunk_incr: int = 5,
         add_eos: bool = True,
         dedup: bool = True,
-        ref_fpu: float = -1
+        ref_fpu: float = -1,
     ) -> FrmTextToSpeechDataset:
         tsv_path = op.join(root, f"{split}.tsv")
         if not op.isfile(tsv_path):
@@ -170,9 +170,7 @@ class FrmTextToSpeechDatasetCreator(TextToSpeechDatasetCreator):
             assert len(s) > 0
 
         ids = [ss[cls.KEY_ID] for ss in s]
-        audio_paths = [
-            op.join(data_cfg.audio_root, ss[cls.KEY_AUDIO]) for ss in s
-        ]
+        audio_paths = [op.join(data_cfg.audio_root, ss[cls.KEY_AUDIO]) for ss in s]
         n_frames = [int(ss[cls.KEY_N_FRAMES]) for ss in s]
         tgt_texts = [ss[cls.KEY_TGT_TEXT] for ss in s]
         src_texts = [ss.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for ss in s]
@@ -203,5 +201,5 @@ class FrmTextToSpeechDatasetCreator(TextToSpeechDatasetCreator):
             chunk_incr=chunk_incr,
             add_eos=add_eos,
             dedup=dedup,
-            ref_fpu=ref_fpu
+            ref_fpu=ref_fpu,
         )

fairseq/data/audio/hubert_dataset.py

@@ -152,10 +152,7 @@ class HubertDataset(FairseqDataset):
             self.label_offsets_list = [
                 load_label_offset(p, inds, tot) for p in label_paths
             ]
-        assert (
-            label_processors is None
-            or len(label_processors) == self.num_labels
-        )
+        assert label_processors is None or len(label_processors) == self.num_labels
         for label_path, label_rate in zip(label_paths, self.label_rates):
             verify_label_lengths(
                 self.sizes, sample_rate, label_path, label_rate, inds, tot
@@ -234,8 +231,7 @@ class HubertDataset(FairseqDataset):
         )
 
         targets_by_label = [
-            [s["label_list"][i] for s in samples]
-            for i in range(self.num_labels)
+            [s["label_list"][i] for s in samples] for i in range(self.num_labels)
         ]
         targets_list, lengths_list, ntokens_list = self.collater_label(
             targets_by_label, audio_size, audio_starts
@@ -270,9 +266,7 @@ class HubertDataset(FairseqDataset):
                 collated_audios[i] = audio
             elif diff < 0:
                 assert self.pad_audio
-                collated_audios[i] = torch.cat(
-                    [audio, audio.new_full((-diff,), 0.0)]
-                )
+                collated_audios[i] = torch.cat([audio, audio.new_full((-diff,), 0.0)])
                 padding_mask[i, diff:] = True
             else:
                 collated_audios[i], audio_starts[i] = self.crop_to_max_size(
@@ -280,9 +274,7 @@ class HubertDataset(FairseqDataset):
                 )
         return collated_audios, padding_mask, audio_starts
 
-    def collater_frm_label(
-        self, targets, audio_size, audio_starts, label_rate, pad
-    ):
+    def collater_frm_label(self, targets, audio_size, audio_starts, label_rate, pad):
         assert label_rate > 0
         s2f = label_rate / self.sample_rate
         frm_starts = [int(round(s * s2f)) for s in audio_starts]
@@ -290,24 +282,20 @@ class HubertDataset(FairseqDataset):
         if not self.pad_audio:
             rem_size = [len(t) - s for t, s in zip(targets, frm_starts)]
             frm_size = min(frm_size, *rem_size)
-        targets = [t[s: s + frm_size] for t, s in zip(targets, frm_starts)]
+        targets = [t[s : s + frm_size] for t, s in zip(targets, frm_starts)]
         logger.debug(f"audio_starts={audio_starts}")
         logger.debug(f"frame_starts={frm_starts}")
         logger.debug(f"frame_size={frm_size}")
 
         lengths = torch.LongTensor([len(t) for t in targets])
         ntokens = lengths.sum().item()
-        targets = data_utils.collate_tokens(
-            targets, pad_idx=pad, left_pad=False
-        )
+        targets = data_utils.collate_tokens(targets, pad_idx=pad, left_pad=False)
         return targets, lengths, ntokens
 
     def collater_seq_label(self, targets, pad):
         lengths = torch.LongTensor([len(t) for t in targets])
         ntokens = lengths.sum().item()
-        targets = data_utils.collate_tokens(
-            targets, pad_idx=pad, left_pad=False
-        )
+        targets = data_utils.collate_tokens(targets, pad_idx=pad, left_pad=False)
         return targets, lengths, ntokens
 
     def collater_label(self, targets_by_label, audio_size, audio_starts):
@@ -315,9 +303,7 @@ class HubertDataset(FairseqDataset):
         itr = zip(targets_by_label, self.label_rates, self.pad_list)
         for targets, label_rate, pad in itr:
             if label_rate == -1:
-                targets, lengths, ntokens = self.collater_seq_label(
-                    targets, pad
-                )
+                targets, lengths, ntokens = self.collater_seq_label(targets, pad)
             else:
                 targets, lengths, ntokens = self.collater_frm_label(
                     targets, audio_size, audio_starts, label_rate, pad

fairseq/data/audio/multi_modality_dataset.py

@@ -29,6 +29,7 @@ class ModalityDatasetItem(NamedTuple):
     max_tokens: Optional[int] = None
     max_sentences: Optional[int] = None
 
+
 # MultiModalityDataset: it concate multiple datasets with different modalities.
 # Compared with ConcatDataset it can 1) sample data given the ratios for different datasets
 # 2) it adds mode to indicate what type of the data samples come from.

fairseq/data/audio/raw_audio_dataset.py

@@ -308,6 +308,7 @@ class FileAudioDataset(RawAudioDataset):
 
     def __getitem__(self, index):
         import soundfile as sf
+
         fn = self.fnames[index]
         fn = fn if isinstance(self.fnames, list) else fn.as_py()
         fn = self.text_compressor.decompress(fn)

fairseq/data/audio/speech_to_text_dataset.py

@@ -45,7 +45,11 @@ def get_features_from_npy_or_audio(path):
 
 
 def get_features_or_waveform_from_stored_zip(
-    path, byte_offset, byte_size, need_waveform=False, use_sample_rate=None,
+    path,
+    byte_offset,
+    byte_size,
+    need_waveform=False,
+    use_sample_rate=None,
 ):
     assert path.endswith(".zip")
     data = read_from_stored_zip(path, byte_offset, byte_size)
@@ -53,18 +57,17 @@ def get_features_or_waveform_from_stored_zip(
     if is_npy_data(data):
         features_or_waveform = np.load(f)
     elif is_sf_audio_data(data):
-        features_or_waveform = \
-            get_waveform(
-                f, always_2d=False, output_sample_rate=use_sample_rate
-            )[0] if need_waveform else get_fbank(f)
+        features_or_waveform = (
+            get_waveform(f, always_2d=False, output_sample_rate=use_sample_rate)[0]
+            if need_waveform
+            else get_fbank(f)
+        )
     else:
         raise ValueError(f'Unknown file format for "{path}"')
     return features_or_waveform
 
 
-def get_features_or_waveform(
-        path: str, need_waveform=False, use_sample_rate=None
-):
+def get_features_or_waveform(path: str, need_waveform=False, use_sample_rate=None):
     """Get speech features from .npy file or waveform from .wav/.flac file.
     The file may be inside an uncompressed ZIP file and is accessed via byte
     offset and length.
@@ -87,8 +90,11 @@ def get_features_or_waveform(
         return get_features_from_npy_or_audio(_path)
     elif len(slice_ptr) == 2:
         features_or_waveform = get_features_or_waveform_from_stored_zip(
-            _path, slice_ptr[0], slice_ptr[1], need_waveform=need_waveform,
-            use_sample_rate=use_sample_rate
+            _path,
+            slice_ptr[0],
+            slice_ptr[1],
+            need_waveform=need_waveform,
+            use_sample_rate=use_sample_rate,
         )
     else:
         raise ValueError(f"Invalid path: {path}")
@@ -145,7 +151,7 @@ class SpeechToTextDataset(FairseqDataset):
         pre_tokenizer=None,
         bpe_tokenizer=None,
         n_frames_per_step=1,
-        speaker_to_id=None
+        speaker_to_id=None,
     ):
         self.split, self.is_train_split = split, is_train_split
         self.cfg = cfg
@@ -235,7 +241,7 @@ class SpeechToTextDataset(FairseqDataset):
         if self.n_frames_per_step == 1:
             return feature
         n_packed_frames = feature.shape[0] // self.n_frames_per_step
-        feature = feature[:self.n_frames_per_step * n_packed_frames]
+        feature = feature[: self.n_frames_per_step * n_packed_frames]
         return feature.reshape(n_packed_frames, -1)
 
     @classmethod
@@ -318,9 +324,11 @@ class SpeechToTextDataset(FairseqDataset):
 
         speaker = None
         if self.speaker_to_id is not None:
-            speaker = torch.tensor(
-                [s.speaker_id for s in samples], dtype=torch.long
-            ).index_select(0, order).view(-1, 1)
+            speaker = (
+                torch.tensor([s.speaker_id for s in samples], dtype=torch.long)
+                .index_select(0, order)
+                .view(-1, 1)
+            )
 
         net_input = {
             "src_tokens": frames,
@@ -388,7 +396,7 @@ class SpeechToTextDatasetCreator(object):
         pre_tokenizer,
         bpe_tokenizer,
         n_frames_per_step,
-        speaker_to_id
+        speaker_to_id,
     ) -> SpeechToTextDataset:
         audio_root = Path(cfg.audio_root)
         ids = [s[cls.KEY_ID] for s in samples]
@@ -415,7 +423,7 @@ class SpeechToTextDatasetCreator(object):
             pre_tokenizer=pre_tokenizer,
             bpe_tokenizer=bpe_tokenizer,
             n_frames_per_step=n_frames_per_step,
-            speaker_to_id=speaker_to_id
+            speaker_to_id=speaker_to_id,
         )
 
     @classmethod
@@ -481,12 +489,19 @@ class SpeechToTextDatasetCreator(object):
         pre_tokenizer,
         bpe_tokenizer,
         n_frames_per_step,
-        speaker_to_id
+        speaker_to_id,
     ) -> SpeechToTextDataset:
         samples = cls._load_samples_from_tsv(root, split)
         return cls._from_list(
-            split, is_train_split, samples, cfg, tgt_dict, pre_tokenizer,
-            bpe_tokenizer, n_frames_per_step, speaker_to_id
+            split,
+            is_train_split,
+            samples,
+            cfg,
+            tgt_dict,
+            pre_tokenizer,
+            bpe_tokenizer,
+            n_frames_per_step,
+            speaker_to_id,
         )
 
     @classmethod
@@ -502,12 +517,19 @@ class SpeechToTextDatasetCreator(object):
         epoch: int,
         seed: int,
         n_frames_per_step: int = 1,
-        speaker_to_id=None
+        speaker_to_id=None,
     ) -> SpeechToTextDataset:
         datasets = [
             cls._from_tsv(
-                root, cfg, split, tgt_dict, is_train_split, pre_tokenizer,
-                bpe_tokenizer, n_frames_per_step, speaker_to_id
+                root,
+                cfg,
+                split,
+                tgt_dict,
+                is_train_split,
+                pre_tokenizer,
+                bpe_tokenizer,
+                n_frames_per_step,
+                speaker_to_id,
             )
             for split in splits.split(",")
         ]

fairseq/data/audio/text_to_speech_dataset.py

@@ -13,8 +13,11 @@ import numpy as np
 import torch
 
 from fairseq.data.audio.speech_to_text_dataset import (
-    SpeechToTextDataset, SpeechToTextDatasetCreator, S2TDataConfig,
-    _collate_frames, get_features_or_waveform
+    SpeechToTextDataset,
+    SpeechToTextDatasetCreator,
+    S2TDataConfig,
+    _collate_frames,
+    get_features_or_waveform,
 )
 from fairseq.data import Dictionary, data_utils as fairseq_data_utils
 
@@ -32,34 +35,44 @@ class TextToSpeechDatasetItem(object):
 
 class TextToSpeechDataset(SpeechToTextDataset):
     def __init__(
-            self,
-            split: str,
-            is_train_split: bool,
-            cfg: S2TDataConfig,
-            audio_paths: List[str],
-            n_frames: List[int],
-            src_texts: Optional[List[str]] = None,
-            tgt_texts: Optional[List[str]] = None,
-            speakers: Optional[List[str]] = None,
-            src_langs: Optional[List[str]] = None,
-            tgt_langs: Optional[List[str]] = None,
-            ids: Optional[List[str]] = None,
-            tgt_dict: Optional[Dictionary] = None,
-            pre_tokenizer=None,
-            bpe_tokenizer=None,
-            n_frames_per_step=1,
-            speaker_to_id=None,
-            durations: Optional[List[List[int]]] = None,
-            pitches: Optional[List[str]] = None,
-            energies: Optional[List[str]] = None
+        self,
+        split: str,
+        is_train_split: bool,
+        cfg: S2TDataConfig,
+        audio_paths: List[str],
+        n_frames: List[int],
+        src_texts: Optional[List[str]] = None,
+        tgt_texts: Optional[List[str]] = None,
+        speakers: Optional[List[str]] = None,
+        src_langs: Optional[List[str]] = None,
+        tgt_langs: Optional[List[str]] = None,
+        ids: Optional[List[str]] = None,
+        tgt_dict: Optional[Dictionary] = None,
+        pre_tokenizer=None,
+        bpe_tokenizer=None,
+        n_frames_per_step=1,
+        speaker_to_id=None,
+        durations: Optional[List[List[int]]] = None,
+        pitches: Optional[List[str]] = None,
+        energies: Optional[List[str]] = None,
     ):
         super(TextToSpeechDataset, self).__init__(
-            split, is_train_split, cfg, audio_paths, n_frames,
-            src_texts=src_texts, tgt_texts=tgt_texts, speakers=speakers,
-            src_langs=src_langs, tgt_langs=tgt_langs, ids=ids,
-            tgt_dict=tgt_dict, pre_tokenizer=pre_tokenizer,
-            bpe_tokenizer=bpe_tokenizer, n_frames_per_step=n_frames_per_step,
-            speaker_to_id=speaker_to_id
+            split,
+            is_train_split,
+            cfg,
+            audio_paths,
+            n_frames,
+            src_texts=src_texts,
+            tgt_texts=tgt_texts,
+            speakers=speakers,
+            src_langs=src_langs,
+            tgt_langs=tgt_langs,
+            ids=ids,
+            tgt_dict=tgt_dict,
+            pre_tokenizer=pre_tokenizer,
+            bpe_tokenizer=bpe_tokenizer,
+            n_frames_per_step=n_frames_per_step,
+            speaker_to_id=speaker_to_id,
         )
         self.durations = durations
         self.pitches = pitches
@@ -84,9 +97,13 @@ class TextToSpeechDataset(SpeechToTextDataset):
                 np.concatenate((energy, [0]))  # pad 0 for EOS
             ).float()
         return TextToSpeechDatasetItem(
-            index=index, source=s2t_item.source, target=s2t_item.target,
-            speaker_id=s2t_item.speaker_id, duration=duration, pitch=pitch,
-            energy=energy
+            index=index,
+            source=s2t_item.source,
+            target=s2t_item.target,
+            speaker_id=s2t_item.speaker_id,
+            duration=duration,
+            pitch=pitch,
+            energy=energy,
         )
 
     def collater(self, samples: List[TextToSpeechDatasetItem]) -> Dict[str, Any]:
@@ -96,8 +113,9 @@ class TextToSpeechDataset(SpeechToTextDataset):
         src_lengths, order = torch.tensor(
             [s.target.shape[0] for s in samples], dtype=torch.long
         ).sort(descending=True)
-        id_ = torch.tensor([s.index for s in samples],
-                           dtype=torch.long).index_select(0, order)
+        id_ = torch.tensor([s.index for s in samples], dtype=torch.long).index_select(
+            0, order
+        )
         feat = _collate_frames(
             [s.source for s in samples], self.cfg.use_audio_input
         ).index_select(0, order)
@@ -115,9 +133,11 @@ class TextToSpeechDataset(SpeechToTextDataset):
 
         speaker = None
         if self.speaker_to_id is not None:
-            speaker = torch.tensor(
-                [s.speaker_id for s in samples], dtype=torch.long
-            ).index_select(0, order).view(-1, 1)
+            speaker = (
+                torch.tensor([s.speaker_id for s in samples], dtype=torch.long)
+                .index_select(0, order)
+                .view(-1, 1)
+            )
 
         bsz, _, d = feat.size()
         prev_output_tokens = torch.cat(
@@ -175,7 +195,7 @@ class TextToSpeechDatasetCreator(SpeechToTextDatasetCreator):
         pre_tokenizer,
         bpe_tokenizer,
         n_frames_per_step,
-        speaker_to_id
+        speaker_to_id,
     ) -> TextToSpeechDataset:
         audio_root = Path(cfg.audio_root)
         ids = [s[cls.KEY_ID] for s in samples]
@@ -189,27 +209,40 @@ class TextToSpeechDatasetCreator(SpeechToTextDatasetCreator):
 
         durations = [s.get(cls.KEY_DURATION, None) for s in samples]
         durations = [
-            None if dd is None else [int(d) for d in dd.split(" ")]
-            for dd in durations
+            None if dd is None else [int(d) for d in dd.split(" ")] for dd in durations
         ]
         durations = None if any(dd is None for dd in durations) else durations
 
         pitches = [s.get(cls.KEY_PITCH, None) for s in samples]
         pitches = [
-            None if pp is None else (audio_root / pp).as_posix()
-            for pp in pitches
+            None if pp is None else (audio_root / pp).as_posix() for pp in pitches
         ]
         pitches = None if any(pp is None for pp in pitches) else pitches
 
         energies = [s.get(cls.KEY_ENERGY, None) for s in samples]
         energies = [
-            None if ee is None else (audio_root / ee).as_posix()
-            for ee in energies]
+            None if ee is None else (audio_root / ee).as_posix() for ee in energies
+        ]
         energies = None if any(ee is None for ee in energies) else energies
 
         return TextToSpeechDataset(
-            split_name, is_train_split, cfg, audio_paths, n_frames,
-            src_texts, tgt_texts, speakers, src_langs, tgt_langs, ids, tgt_dict,
-            pre_tokenizer, bpe_tokenizer, n_frames_per_step, speaker_to_id,
-            durations, pitches, energies
+            split_name,
+            is_train_split,
+            cfg,
+            audio_paths,
+            n_frames,
+            src_texts,
+            tgt_texts,
+            speakers,
+            src_langs,
+            tgt_langs,
+            ids,
+            tgt_dict,
+            pre_tokenizer,
+            bpe_tokenizer,
+            n_frames_per_step,
+            speaker_to_id,
+            durations,
+            pitches,
+            energies,
         )

fairseq/data/colorize_dataset.py

@@ -9,7 +9,7 @@ from . import BaseWrapperDataset
 
 
 class ColorizeDataset(BaseWrapperDataset):
-    """ Adds 'colors' property to net input that is obtained from the provided color getter for use by models """
+    """Adds 'colors' property to net input that is obtained from the provided color getter for use by models"""
 
     def __init__(self, dataset, color_getter):
         super().__init__(dataset)

fairseq/data/data_utils.py

@@ -69,6 +69,7 @@ def collate_tokens(
         copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
     return res
 
+
 def load_indexed_dataset(
     path, dictionary=None, dataset_impl=None, combine=False, default="cached"
 ):
@@ -324,9 +325,7 @@ def batch_by_size(
         )
 
     # added int() to avoid TypeError: an integer is required
-    max_tokens = (
-        int(max_tokens) if max_tokens is not None else -1
-    )
+    max_tokens = int(max_tokens) if max_tokens is not None else -1
     max_sentences = max_sentences if max_sentences is not None else -1
     bsz_mult = required_batch_size_multiple
 
@@ -375,8 +374,9 @@ def post_process(sentence: str, symbol: str):
         sentence = sentence.replace(" ", "").replace("|", " ").strip()
     elif symbol == "silence":
         import re
+
         sentence = sentence.replace("<SIL>", "")
-        sentence = re.sub(' +', ' ', sentence).strip()
+        sentence = re.sub(" +", " ", sentence).strip()
     elif symbol == "_EOW":
         sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
     elif symbol in {"subword_nmt", "@@ ", "@@"}:
@@ -547,7 +547,7 @@ def get_buckets(sizes, num_buckets):
         np.percentile(
             sizes,
             np.linspace(0, 100, num_buckets + 1),
-            interpolation='lower',
+            interpolation="lower",
         )[1:]
     )
     return buckets
@@ -564,7 +564,6 @@ def get_bucketed_sizes(orig_sizes, buckets):
     return sizes
 
 
-
 def _find_extra_valid_paths(dataset_path: str) -> set:
     paths = utils.split_paths(dataset_path)
     all_valid_paths = set()

fairseq/data/encoders/sentencepiece_bpe.py

@@ -21,8 +21,10 @@ class SentencepieceConfig(FairseqDataclass):
     )
     sentencepiece_alpha: Optional[float] = field(
         default=None,
-        metadata={"help": "soothing parameter for unigram sampling, "
-                          "and merge probability for BPE-dropout"}
+        metadata={
+            "help": "soothing parameter for unigram sampling, "
+            "and merge probability for BPE-dropout"
+        },
     )
 
 
@@ -45,8 +47,7 @@ class SentencepieceBPE(object):
     def encode(self, x: str) -> str:
         return " ".join(
             self.sp.Encode(
-                x, out_type=str, enable_sampling=self.enable_sampling,
-                alpha=self.alpha
+                x, out_type=str, enable_sampling=self.enable_sampling, alpha=self.alpha
             )
         )
 

fairseq/data/fairseq_dataset.py

@@ -138,7 +138,7 @@ class FairseqDataset(torch.utils.data.Dataset, EpochListening):
             )
 
         try:
-            num_tokens_vec = self.num_tokens_vec(indices).astype('int64')
+            num_tokens_vec = self.num_tokens_vec(indices).astype("int64")
         except NotImplementedError:
             num_tokens_vec = None
 

fairseq/data/huffman/huffman_coder.py

@@ -140,7 +140,9 @@ class HuffmanNode:
     def is_leaf(self) -> bool:
         return self.left is None and self.right is None
 
-    def code_table(self, prefix: tp.Optional[bitarray] = None) -> tp.Dict[str, "HuffmanNode"]:
+    def code_table(
+        self, prefix: tp.Optional[bitarray] = None
+    ) -> tp.Dict[str, "HuffmanNode"]:
         defaulted_prefix = prefix if prefix is not None else bitarray()
         if self.is_leaf():
             self.code = (

fairseq/data/indexed_dataset.py

@@ -67,7 +67,9 @@ def make_builder(out_file, impl, vocab_size=None):
     elif impl == "fasta":
         raise NotImplementedError
     elif impl == "huffman":
-        raise ValueError("Use HuffmanCodeBuilder directly as it has a different interface.")
+        raise ValueError(
+            "Use HuffmanCodeBuilder directly as it has a different interface."
+        )
     else:
         return IndexedDatasetBuilder(out_file)
 

fairseq/data/iterators.py

@@ -380,7 +380,9 @@ class EpochBatchIterator(EpochBatchIterating):
                 # reset _frozen_batches to refresh the next epoch
                 self._frozen_batches = None
             self._cur_epoch_itr = self._get_iterator_for_epoch(
-                self.epoch, shuffle, fix_batches_to_gpus=fix_batches_to_gpus,
+                self.epoch,
+                shuffle,
+                fix_batches_to_gpus=fix_batches_to_gpus,
             )
         self.shuffle = shuffle
         return self._cur_epoch_itr
@@ -421,7 +423,9 @@ class EpochBatchIterator(EpochBatchIterating):
         if itr_pos > 0:
             # fast-forward epoch iterator
             self._next_epoch_itr = self._get_iterator_for_epoch(
-                self.epoch, shuffle=state_dict.get("shuffle", True), offset=itr_pos,
+                self.epoch,
+                shuffle=state_dict.get("shuffle", True),
+                offset=itr_pos,
             )
             if self._next_epoch_itr is None:
                 if version == 1:

fairseq/data/language_pair_dataset.py

@@ -114,7 +114,10 @@ def collate(
         "id": id,
         "nsentences": len(samples),
         "ntokens": ntokens,
-        "net_input": {"src_tokens": src_tokens, "src_lengths": src_lengths,},
+        "net_input": {
+            "src_tokens": src_tokens,
+            "src_lengths": src_lengths,
+        },
         "target": target,
     }
     if prev_output_tokens is not None:
@@ -467,5 +470,8 @@ class LanguagePairDataset(FairseqDataset):
             list: list of removed indices
         """
         return data_utils.filter_paired_dataset_indices_by_size(
-            self.src_sizes, self.tgt_sizes, indices, max_sizes,
+            self.src_sizes,
+            self.tgt_sizes,
+            indices,
+            max_sizes,
         )

fairseq/data/multi_corpus_dataset.py

@@ -80,7 +80,9 @@ class MultiCorpusDataset(FairseqDataset):
     def ordered_indices(self):
         start = time.time()
         with data_utils.numpy_seed(self.seed, self.epoch):
-            logger.info(f"sampling new dataset with seed {self.seed} epoch {self.epoch}")
+            logger.info(
+                f"sampling new dataset with seed {self.seed} epoch {self.epoch}"
+            )
             sampled_indices = []
             num_selected_instances = 0
 

fairseq/data/multilingual/multilingual_data_manager.py

@@ -40,8 +40,8 @@ from fairseq.utils import FileContentsAction, csv_str_list, eval_str_dict
 
 logger = logging.getLogger(__name__)
 
-SRC_DICT_NAME = 'src'
-TGT_DICT_NAME = 'tgt'
+SRC_DICT_NAME = "src"
+TGT_DICT_NAME = "tgt"
 
 
 def _lang_id(dic: Dictionary, lang: str):
@@ -64,14 +64,16 @@ class MultilingualDatasetManager(object):
         self.seed = args.seed
         self.lang_pairs = lang_pairs
         self.extra_lang_pairs = (
-                list(
-                    {p for _, v in args.extra_lang_pairs.items() for p in v.split(",")}
-                )
-                if args.extra_lang_pairs
-                else []
-            )
-        self.src_langs = {p.split("-")[0] for p in args.lang_pairs + self.extra_lang_pairs}
-        self.tgt_langs = {p.split("-")[1] for p in args.lang_pairs + self.extra_lang_pairs}
+            list({p for _, v in args.extra_lang_pairs.items() for p in v.split(",")})
+            if args.extra_lang_pairs
+            else []
+        )
+        self.src_langs = {
+            p.split("-")[0] for p in args.lang_pairs + self.extra_lang_pairs
+        }
+        self.tgt_langs = {
+            p.split("-")[1] for p in args.lang_pairs + self.extra_lang_pairs
+        }
         self.langs = langs
         self.dicts = dicts
         self.lang_dict = self.create_lang_dictionary(self.langs)
@@ -111,10 +113,18 @@ class MultilingualDatasetManager(object):
             "note that the ordering determines language token IDs; "
             "--langs and --lang-dict are two exclusive options",
         )
-        parser.add_argument('--source-dict', default=None, type=str,
-                            help='path to source dictionary; if specified it will override per language dictionary loading')
-        parser.add_argument('--target-dict', default=None, type=str,
-                            help='path to target dictionary; if specified it will override per language dictionary loading')
+        parser.add_argument(
+            "--source-dict",
+            default=None,
+            type=str,
+            help="path to source dictionary; if specified it will override per language dictionary loading",
+        )
+        parser.add_argument(
+            "--target-dict",
+            default=None,
+            type=str,
+            help="path to target dictionary; if specified it will override per language dictionary loading",
+        )
         parser.add_argument(
             "--lang-tok-style",
             default=LangTokStyle.multilingual.value,
@@ -378,7 +388,9 @@ class MultilingualDatasetManager(object):
             )
             return d
 
-        dicts = cls.load_all_dictionaries(args, language_list, load_dictionary_and_postproc, training)
+        dicts = cls.load_all_dictionaries(
+            args, language_list, load_dictionary_and_postproc, training
+        )
         return language_list, dicts, training
 
     @classmethod
@@ -424,7 +436,10 @@ class MultilingualDatasetManager(object):
 
         if args.fixed_dictionary is not None:
             fixed_dict = load_dictionary(args.fixed_dictionary)
-            dicts = {lang: fixed_dict for lang in src_langs_to_load_dicts + tgt_langs_to_load_dicts}
+            dicts = {
+                lang: fixed_dict
+                for lang in src_langs_to_load_dicts + tgt_langs_to_load_dicts
+            }
         else:
             if args.source_dict is None:
                 load_dicts(src_langs_to_load_dicts)
@@ -477,7 +492,10 @@ class MultilingualDatasetManager(object):
                 lang=tgt_lang, lang_tok_style=self.args.lang_tok_style, spec=spec
             )
         return self.get_langtok_index(
-            langtok, self.get_source_dictionary(src_lang) if src_lang else self.get_target_dictionary(tgt_lang)
+            langtok,
+            self.get_source_dictionary(src_lang)
+            if src_lang
+            else self.get_target_dictionary(tgt_lang),
         )
 
     def get_decoder_langtok(self, tgt_lang, spec=None):
@@ -819,7 +837,9 @@ class MultilingualDatasetManager(object):
         if self.args.lang_tok_replacing_bos_eos:
             ds = self.alter_dataset_langtok(
                 langpair_ds,
-                src_eos=self.get_source_dictionary(src).eos() if src else self.get_target_dictionary(tgt).eos(),
+                src_eos=self.get_source_dictionary(src).eos()
+                if src
+                else self.get_target_dictionary(tgt).eos(),
                 src_lang=src,
                 tgt_eos=self.get_target_dictionary(tgt).eos(),
                 tgt_lang=tgt,

fairseq/data/noising.py

@@ -298,7 +298,6 @@ class NoisingDataset(torch.utils.data.Dataset):
         )
         self.sizes = src_dataset.sizes
 
-
     def __getitem__(self, index):
         """
         Returns a single noisy sample. Multiple samples are fed to the collater

fairseq/data/text_compressor.py

@@ -14,8 +14,7 @@ class TextCompressionLevel(Enum):
 
 class TextCompressor(object):
     def __init__(
-            self, level: TextCompressionLevel,
-            max_input_byte_length: int = 2 ** 16
+        self, level: TextCompressionLevel, max_input_byte_length: int = 2 ** 16
     ):
         self.level = level
         self.max_input_length = max_input_byte_length
@@ -23,11 +22,13 @@ class TextCompressor(object):
     def compress(self, text: str) -> bytes:
         if self.level == TextCompressionLevel.low:
             import zlib
+
             # zlib: built-in, fast
             return zlib.compress(text.encode(), level=0)
         elif self.level == TextCompressionLevel.high:
             try:
                 import unishox2
+
                 # unishox2: optimized for short text but slower
             except ImportError:
                 raise ImportError(
@@ -42,6 +43,7 @@ class TextCompressor(object):
     def decompress(self, compressed: bytes) -> str:
         if self.level == TextCompressionLevel.low:
             import zlib
+
             return zlib.decompress(compressed).decode()
         elif self.level == TextCompressionLevel.high:
             try:

fairseq/data/token_block_dataset.py

@@ -69,7 +69,10 @@ class TokenBlockDataset(FairseqDataset):
                 _sizes, split_path, (plasma_id, 1), plasma_path=plasma_path
             )
             self._block_to_dataset_index = plasma_utils.PlasmaView(
-                block_to_dataset_index, split_path, (plasma_id, 2), plasma_path=plasma_path,
+                block_to_dataset_index,
+                split_path,
+                (plasma_id, 2),
+                plasma_path=plasma_path,
             )
         else:
             self._slice_indices = plasma_utils.PlasmaArray(slice_indices)
@@ -127,7 +130,8 @@ class TokenBlockDataset(FairseqDataset):
             )
         else:
             block_to_dataset_index = _get_block_to_dataset_index_fast(
-                sizes, slice_indices,
+                sizes,
+                slice_indices,
             )
         size_dtype = np.uint16 if block_size < 65535 else np.uint32
         num_tokens = slice_indices[-1].max()

fairseq/data/transform_eos_lang_pair_dataset.py

@@ -52,7 +52,7 @@ class TransformEosLangPairDataset(FairseqDataset):
         if len(samples) == 0:
             return samples
 
-        if 'net_input' not in samples:
+        if "net_input" not in samples:
             return samples
 
         if self.new_src_eos is not None:

fairseq/dataclass/configs.py

@@ -126,7 +126,8 @@ class CommonConfig(FairseqDataclass):
         metadata={"help": "Weights and Biases project name to use for logging"},
     )
     azureml_logging: Optional[bool] = field(
-        default=False, metadata={"help": "Log scalars to AzureML context"},
+        default=False,
+        metadata={"help": "Log scalars to AzureML context"},
     )
     seed: int = field(
         default=1, metadata={"help": "pseudo random number generator seed"}
@@ -428,19 +429,23 @@ class DistributedTrainingConfig(FairseqDataclass):
     tpu: bool = II("common.tpu")
     # configuration for --ddp-backend=fully_sharded
     no_reshard_after_forward: bool = field(
-        default=False, metadata={"help": "don't reshard parameters after forward pass"},
+        default=False,
+        metadata={"help": "don't reshard parameters after forward pass"},
     )
     fp32_reduce_scatter: bool = field(
-        default=False, metadata={"help": "reduce-scatter grads in FP32"},
+        default=False,
+        metadata={"help": "reduce-scatter grads in FP32"},
     )
     cpu_offload: bool = field(
         default=False, metadata={"help": "offload FP32 params to CPU"}
     )
     use_sharded_state: bool = field(
-        default=False, metadata={"help": "use sharded checkpoint files"},
+        default=False,
+        metadata={"help": "use sharded checkpoint files"},
     )
     not_fsdp_flatten_parameters: bool = field(
-        default=False, metadata={"help": "not flatten parameter param for fsdp"},
+        default=False,
+        metadata={"help": "not flatten parameter param for fsdp"},
     )
 
 
@@ -786,10 +791,12 @@ class FairseqBMUFConfig(FairseqDataclass):
 @dataclass
 class GenerationConfig(FairseqDataclass):
     beam: int = field(
-        default=5, metadata={"help": "beam size"},
+        default=5,
+        metadata={"help": "beam size"},
     )
     nbest: int = field(
-        default=1, metadata={"help": "number of hypotheses to output"},
+        default=1,
+        metadata={"help": "number of hypotheses to output"},
     )
     max_len_a: float = field(
         default=0,
@@ -804,19 +811,24 @@ class GenerationConfig(FairseqDataclass):
         },
     )
     min_len: int = field(
-        default=1, metadata={"help": "minimum generation length"},
+        default=1,
+        metadata={"help": "minimum generation length"},
     )
     match_source_len: bool = field(
-        default=False, metadata={"help": "generations should match the source length"},
+        default=False,
+        metadata={"help": "generations should match the source length"},
     )
     unnormalized: bool = field(
-        default=False, metadata={"help": "compare unnormalized hypothesis scores"},
+        default=False,
+        metadata={"help": "compare unnormalized hypothesis scores"},
     )
     no_early_stop: bool = field(
-        default=False, metadata={"help": "deprecated"},
+        default=False,
+        metadata={"help": "deprecated"},
     )
     no_beamable_mm: bool = field(
-        default=False, metadata={"help": "don't use BeamableMM in attention layers"},
+        default=False,
+        metadata={"help": "don't use BeamableMM in attention layers"},
     )
     lenpen: float = field(
         default=1,
@@ -838,10 +850,12 @@ class GenerationConfig(FairseqDataclass):
         },
     )
     sacrebleu: bool = field(
-        default=False, metadata={"help": "score with sacrebleu"},
+        default=False,
+        metadata={"help": "score with sacrebleu"},
     )
     score_reference: bool = field(
-        default=False, metadata={"help": "just score the reference translation"},
+        default=False,
+        metadata={"help": "just score the reference translation"},
     )
     prefix_size: int = field(
         default=0,
@@ -875,10 +889,12 @@ class GenerationConfig(FairseqDataclass):
         },
     )
     temperature: float = field(
-        default=1.0, metadata={"help": "temperature for generation"},
+        default=1.0,
+        metadata={"help": "temperature for generation"},
     )
     diverse_beam_groups: int = field(
-        default=-1, metadata={"help": "number of groups for Diverse Beam Search"},
+        default=-1,
+        metadata={"help": "number of groups for Diverse Beam Search"},
     )
     diverse_beam_strength: float = field(
         default=0.5,
@@ -897,13 +913,16 @@ class GenerationConfig(FairseqDataclass):
         },
     )
     print_step: bool = field(
-        default=False, metadata={"help": "print steps"},
+        default=False,
+        metadata={"help": "print steps"},
     )
     lm_path: Optional[str] = field(
-        default=None, metadata={"help": "path to lm checkpoint for lm fusion"},
+        default=None,
+        metadata={"help": "path to lm checkpoint for lm fusion"},
     )
     lm_weight: float = field(
-        default=0.0, metadata={"help": "weight for lm probs for lm fusion"},
+        default=0.0,
+        metadata={"help": "weight for lm probs for lm fusion"},
     )
 
     # arguments for iterative refinement generator
@@ -912,7 +931,8 @@ class GenerationConfig(FairseqDataclass):
         metadata={"help": "if > 0.0, it penalized early-stopping in decoding."},
     )
     iter_decode_max_iter: int = field(
-        default=10, metadata={"help": "maximum iterations for iterative refinement."},
+        default=10,
+        metadata={"help": "maximum iterations for iterative refinement."},
     )
     iter_decode_force_max_iter: bool = field(
         default=False,
@@ -939,7 +959,8 @@ class GenerationConfig(FairseqDataclass):
         },
     )
     retain_dropout: bool = field(
-        default=False, metadata={"help": "Use dropout at inference time"},
+        default=False,
+        metadata={"help": "Use dropout at inference time"},
     )
     # temporarily set to Any until https://github.com/facebookresearch/hydra/issues/1117 is fixed
     # retain_dropout_modules: Optional[List[str]] = field(
@@ -964,7 +985,8 @@ class GenerationConfig(FairseqDataclass):
 @dataclass
 class CommonEvalConfig(FairseqDataclass):
     path: Optional[str] = field(
-        default=None, metadata={"help": "path(s) to model file(s), colon separated"},
+        default=None,
+        metadata={"help": "path(s) to model file(s), colon separated"},
     )
     post_process: Optional[str] = field(
         default=None,
@@ -1026,7 +1048,8 @@ class InteractiveConfig(FairseqDataclass):
         },
     )
     input: str = field(
-        default="-", metadata={"help": "file to read from; use - for stdin"},
+        default="-",
+        metadata={"help": "file to read from; use - for stdin"},
     )
 
 

fairseq/dataclass/constants.py

@@ -35,14 +35,16 @@ def ChoiceEnum(choices: List[str]):
 
 
 LOG_FORMAT_CHOICES = ChoiceEnum(["json", "none", "simple", "tqdm"])
-DDP_BACKEND_CHOICES = ChoiceEnum([
-    "c10d",  # alias for pytorch_ddp
-    "fully_sharded",  # FullyShardedDataParallel from fairscale
-    "legacy_ddp",
-    "no_c10d",  # alias for legacy_ddp
-    "pytorch_ddp",
-    "slowmo",
-])
+DDP_BACKEND_CHOICES = ChoiceEnum(
+    [
+        "c10d",  # alias for pytorch_ddp
+        "fully_sharded",  # FullyShardedDataParallel from fairscale
+        "legacy_ddp",
+        "no_c10d",  # alias for legacy_ddp
+        "pytorch_ddp",
+        "slowmo",
+    ]
+)
 DDP_COMM_HOOK_CHOICES = ChoiceEnum(["none", "fp16"])
 DATASET_IMPL_CHOICES = ChoiceEnum(["raw", "lazy", "cached", "mmap", "fasta", "huffman"])
 GENERATION_CONSTRAINTS_CHOICES = ChoiceEnum(["ordered", "unordered"])

fairseq/dataclass/initialize.py

@@ -28,7 +28,7 @@ def hydra_init(cfg_name="config") -> None:
 
 
 def add_defaults(cfg: DictConfig) -> None:
-    """This function adds default values that are stored in dataclasses that hydra doesn't know about """
+    """This function adds default values that are stored in dataclasses that hydra doesn't know about"""
 
     from fairseq.registry import REGISTRIES
     from fairseq.tasks import TASK_DATACLASS_REGISTRY

fairseq/dataclass/utils.py

@@ -57,21 +57,21 @@ def gen_parser_from_dataclass(
     with_prefix: Optional[str] = None,
 ) -> None:
     """
-        convert a dataclass instance to tailing parser arguments.
+    convert a dataclass instance to tailing parser arguments.
 
-        If `with_prefix` is provided, prefix all the keys in the resulting parser with it. It means that we are
-        building a flat namespace from a structured dataclass (see transformer_config.py for example).
+    If `with_prefix` is provided, prefix all the keys in the resulting parser with it. It means that we are
+    building a flat namespace from a structured dataclass (see transformer_config.py for example).
     """
 
     def argparse_name(name: str):
-        if name == "data" and (with_prefix is None or with_prefix == ''):
+        if name == "data" and (with_prefix is None or with_prefix == ""):
             # normally data is positional args, so we don't add the -- nor the prefix
             return name
         if name == "_name":
             # private member, skip
             return None
         full_name = "--" + name.replace("_", "-")
-        if with_prefix is not None and with_prefix != '':
+        if with_prefix is not None and with_prefix != "":
             # if a prefix is specified, construct the prefixed arg name
             full_name = with_prefix + "-" + full_name[2:]  # strip -- when composing
         return full_name
@@ -143,8 +143,8 @@ def gen_parser_from_dataclass(
                     kwargs["default"] = field_default
 
         # build the help with the hierarchical prefix
-        if with_prefix is not None and with_prefix != '' and field_help is not None:
-            field_help = with_prefix[2:] + ': ' + field_help
+        if with_prefix is not None and with_prefix != "" and field_help is not None:
+            field_help = with_prefix[2:] + ": " + field_help
 
         kwargs["help"] = field_help
         if field_const is not None:

fairseq/distributed/__init__.py

@@ -4,7 +4,11 @@
 # LICENSE file in the root directory of this source tree.
 
 from .distributed_timeout_wrapper import DistributedTimeoutWrapper
-from .fully_sharded_data_parallel import fsdp_enable_wrap, fsdp_wrap, FullyShardedDataParallel
+from .fully_sharded_data_parallel import (
+    fsdp_enable_wrap,
+    fsdp_wrap,
+    FullyShardedDataParallel,
+)
 from .legacy_distributed_data_parallel import LegacyDistributedDataParallel
 from .module_proxy_wrapper import ModuleProxyWrapper
 from .tpu_distributed_data_parallel import TPUDistributedDataParallel

fairseq/distributed/distributed_timeout_wrapper.py

@@ -33,6 +33,7 @@ class DistributedTimeoutWrapper(nn.Module):
             (set to a value <= 0 to disable the timeout)
         signal (Optional): signal to send once timeout is triggered
     """
+
     def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT):
         super().__init__()
         self.module = module
@@ -86,9 +87,11 @@ class DistributedTimeoutWrapper(nn.Module):
             if self._terminated:
                 break
             elif not success:
-                logger.error((
-                    "Killing job for not making progress in {} seconds. "
-                    "Set --heartbeat-timeout=-1 to disable this timeout."
-                ).format(int(self.timeout)))
+                logger.error(
+                    (
+                        "Killing job for not making progress in {} seconds. "
+                        "Set --heartbeat-timeout=-1 to disable this timeout."
+                    ).format(int(self.timeout))
+                )
                 os.kill(parent_pid, self.signal)
                 return

fairseq/distributed/legacy_distributed_data_parallel.py

@@ -137,7 +137,7 @@ class LegacyDistributedDataParallel(nn.Module):
                     if param.grad is None:
                         param.grad = torch.zeros_like(param)
 
-                    if hasattr(param, 'expert'):
+                    if hasattr(param, "expert"):
                         # Skip gradient sync for unshared parameters
                         continue
 

fairseq/distributed/module_proxy_wrapper.py

@@ -26,8 +26,9 @@ class ModuleProxyWrapper(nn.Module):
 
     def __init__(self, module: nn.Module):
         super().__init__()
-        assert hasattr(module, "module"), \
-            "ModuleProxyWrapper expects input to wrap another module"
+        assert hasattr(
+            module, "module"
+        ), "ModuleProxyWrapper expects input to wrap another module"
         self.module = module
 
     def __getattr__(self, name):

fairseq/distributed/tpu_distributed_data_parallel.py

@@ -10,7 +10,6 @@ from fairseq.distributed import utils
 
 
 class TPUDistributedDataParallel(nn.Module):
-
     def __init__(self, module, process_group):
         super().__init__()
         self.module = module
@@ -35,9 +34,10 @@ class TPUDistributedDataParallel(nn.Module):
             gradients.append(p.grad)
 
         import torch_xla.core.xla_model as xm
+
         xm.all_reduce(
-            'sum',
+            "sum",
             gradients,
-            scale=1. / self.world_size,
+            scale=1.0 / self.world_size,
             groups=self.process_group[1],
         )

fairseq/distributed/utils.py

@@ -201,9 +201,7 @@ def _pipeline_parallel_post_init(
         # distributed_world_size to be based on the total number of GPUs, so
         # we need to correct them to be based on the number of pipelines.
         assert cfg.distributed_world_size % num_pipeline_devices == 0
-        cfg.distributed_world_size = (
-            cfg.distributed_world_size // num_pipeline_devices
-        )
+        cfg.distributed_world_size = cfg.distributed_world_size // num_pipeline_devices
         # In the case of 4-way MP on nodes with 8 GPUs, we want
         # distributed_rank to be the starting GPU index for each pipeline
         # i.e., 0, 2, ...
@@ -306,8 +304,10 @@ def distributed_init(cfg: FairseqConfig):
         model_part_number = get_model_parallel_rank()
         cfg.checkpoint.checkpoint_suffix += "-model_part-{0}".format(model_part_number)
 
-    if hasattr(cfg,  "model") and getattr(cfg.model, "base_layers", 0) > 0:
-        cfg.checkpoint.checkpoint_suffix = f"-rank-{cfg.distributed_training.distributed_rank}"
+    if hasattr(cfg, "model") and getattr(cfg.model, "base_layers", 0) > 0:
+        cfg.checkpoint.checkpoint_suffix = (
+            f"-rank-{cfg.distributed_training.distributed_rank}"
+        )
 
     return cfg.distributed_training.distributed_rank
 
@@ -696,7 +696,7 @@ def broadcast_tensors(
             dist_device = torch.device("cpu")
 
     # share metadata first to simplify transfer
-    is_src_rank = (get_rank(group) == src_rank)
+    is_src_rank = get_rank(group) == src_rank
     if is_src_rank:
         metadata = [
             {"size": t.size(), "dtype": t.dtype, "device": t.device} for t in tensors
@@ -747,7 +747,10 @@ def broadcast_object(
 
 
 def _broadcast_object_slow(
-    obj: Any, src_rank: int, group: object, dist_device: torch.device,
+    obj: Any,
+    src_rank: int,
+    group: object,
+    dist_device: torch.device,
 ) -> Any:
     if get_rank(group) == src_rank:
         # Emit data

fairseq/file_io.py

@@ -152,6 +152,7 @@ class PathManager:
     """
     ioPath async PathManager methods:
     """
+
     @staticmethod
     def opena(
         path: str,
@@ -169,6 +170,7 @@ class PathManager:
             logging.info("ioPath is initializing PathManager.")
             try:
                 from iopath.common.file_io import PathManager
+
                 IOPathManager = PathManager()
             except Exception:
                 logging.exception("Failed to initialize ioPath PathManager object.")

fairseq/file_utils.py

@@ -146,6 +146,7 @@ def cached_path_from_pm(url_or_filename):
     """
     try:
         from fairseq.file_io import PathManager
+
         local_path = PathManager.get_local_path(url_or_filename)
         return local_path
     except Exception:

fairseq/logging/metrics.py

@@ -130,6 +130,7 @@ def log_scalar(
             agg.add_meter(key, AverageMeter(round=round), priority)
         agg[key].update(value, weight)
 
+
 def log_scalar_sum(
     key: str,
     value: float,
@@ -309,6 +310,7 @@ def load_state_dict(state_dict):
 def xla_metrics_report():
     try:
         import torch_xla.debug.metrics as met
+
         print(met.metrics_report())
     except ImportError:
         return

fairseq/model_parallel/megatron_trainer.py

@@ -52,8 +52,7 @@ class MegatronTrainer(Trainer):
 
     def save_checkpoint(self, filename, extra_state):
         """Save all training state in a checkpoint file."""
-        extra_state['rng_tracker_states'] \
-            = get_cuda_rng_tracker().get_states()
+        extra_state["rng_tracker_states"] = get_cuda_rng_tracker().get_states()
         super().save_checkpoint(filename, extra_state)
 
     def load_checkpoint(
@@ -64,8 +63,13 @@ class MegatronTrainer(Trainer):
         optimizer_overrides=None,
         reset_meters=False,
     ):
-        extra_state = super().load_checkpoint(filename, reset_optimizer=reset_optimizer, reset_lr_scheduler=reset_lr_scheduler, optimizer_overrides=optimizer_overrides, reset_meters=reset_meters)
-        if extra_state is not None and 'rng_tracker_states' in extra_state:
-            get_cuda_rng_tracker().set_states(
-                extra_state['rng_tracker_states'])
+        extra_state = super().load_checkpoint(
+            filename,
+            reset_optimizer=reset_optimizer,
+            reset_lr_scheduler=reset_lr_scheduler,
+            optimizer_overrides=optimizer_overrides,
+            reset_meters=reset_meters,
+        )
+        if extra_state is not None and "rng_tracker_states" in extra_state:
+            get_cuda_rng_tracker().set_states(extra_state["rng_tracker_states"])
         return extra_state

fairseq/model_parallel/models/pipeline_parallel_transformer/layers.py

@@ -9,6 +9,7 @@ from collections import namedtuple
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
 from fairseq import options, utils
 from fairseq.modules import (
     AdaptiveSoftmax,
@@ -17,7 +18,6 @@ from fairseq.modules import (
     PositionalEmbedding,
 )
 
-
 EncoderOut = namedtuple(
     "TransformerEncoderOut",
     [
@@ -30,7 +30,7 @@ EncoderOut = namedtuple(
 
 
 class TransformerEncoderEmbedding(nn.Module):
-    """ Encoder Embedding + Positional Embedding """
+    """Encoder Embedding + Positional Embedding"""
 
     def __init__(self, args, embed_tokens):
         super().__init__()
@@ -109,7 +109,7 @@ class TransformerEncoderLayerNorm(nn.Module):
 
 
 class TransformerDecoderEmbedding(nn.Module):
-    """ Decoder Embedding + Positional Embedding """
+    """Decoder Embedding + Positional Embedding"""
 
     def __init__(self, args, embed_tokens):
         super().__init__()

fairseq/model_parallel/models/pipeline_parallel_transformer/model.py

@@ -42,16 +42,20 @@ DEFAULT_MAX_TARGET_POSITIONS = 1024
 TORCH_PIPE = False
 RPC_INIT = False
 
+
 def import_pipe():
     global TORCH_PIPE
     global RPC_INIT
     try:
-        from torch.distributed.pipeline.sync import Pipe # noqa
+        from torch.distributed.pipeline.sync import Pipe  # noqa
+
         global Pipe
         from torch.distributed.pipeline.sync.utils import partition_model
+
         global partition_model
         from torch.distributed import rpc
         import tempfile
+
         TORCH_PIPE = True
         # Initialize single process RPC agent since TORCH_PIPE requires
         # RRef. RRef depends on RPC being initialized and as a result we initialize
@@ -64,14 +68,15 @@ def import_pipe():
                 world_size=1,
                 rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
                     init_method="file://{}".format(tmpfile.name),
-                )
+                ),
             )
             RPC_INIT = True
-        logger.info('Using torch pipe')
+        logger.info("Using torch pipe")
     except ImportError:
         try:
-            from fairscale.nn import Pipe # noqa
-            logger.info('Using fairscale pipe')
+            from fairscale.nn import Pipe  # noqa
+
+            logger.info("Using fairscale pipe")
         except ImportError:
             raise ImportError("Please install fairscale with: pip install fairscale")
 
@@ -153,9 +158,14 @@ class PipelineParallelTransformerModel(BaseFairseqModel):
                     decoder_module_list.append(module)
                 module_count += 1
         self.model = None
-        self.encoder = TransformerEncoder(cfg.distributed_training, None, None, encoder_module_list)
+        self.encoder = TransformerEncoder(
+            cfg.distributed_training, None, None, encoder_module_list
+        )
         self.decoder = TransformerDecoder(
-            cfg.distributed_training, None, None, decoder_module_list=decoder_module_list
+            cfg.distributed_training,
+            None,
+            None,
+            decoder_module_list=decoder_module_list,
         )
 
     @staticmethod
@@ -471,7 +481,9 @@ class TransformerEncoder(FairseqEncoder):
         self.use_pipeline = encoder_module_list is not None
         if not self.use_pipeline:
             self.embedding_layer = TransformerEncoderEmbedding(args, embed_tokens)
-            self.encoder_layers = nn.Sequential(*[TransformerEncoderLayer(args) for i in range(args.encoder_layers)])
+            self.encoder_layers = nn.Sequential(
+                *[TransformerEncoderLayer(args) for i in range(args.encoder_layers)]
+            )
             if isinstance(embed_tokens, nn.ModuleList):
                 emb_dim = sum(e.embedding_dim for e in embed_tokens)
             else:
@@ -490,7 +502,11 @@ class TransformerEncoder(FairseqEncoder):
             )
             if TORCH_PIPE:
                 self.model = Pipe(
-                    module=partition_model(nn.Sequential(*encoder_module_list), encoder_balance, encoder_devices),
+                    module=partition_model(
+                        nn.Sequential(*encoder_module_list),
+                        encoder_balance,
+                        encoder_devices,
+                    ),
                     chunks=args.pipeline_chunks,
                     checkpoint=args.pipeline_checkpoint,
                 )
@@ -614,10 +630,12 @@ class TransformerDecoder(FairseqDecoder):
         self.use_pipeline = decoder_module_list is not None
         if not self.use_pipeline:
             self.embedding_layer = TransformerDecoderEmbedding(args, embed_tokens)
-            self.decoder_layers = nn.Sequential(*[
-                TransformerDecoderLayer(args, no_encoder_attn)
-                for _ in range(args.decoder_layers)
-            ])
+            self.decoder_layers = nn.Sequential(
+                *[
+                    TransformerDecoderLayer(args, no_encoder_attn)
+                    for _ in range(args.decoder_layers)
+                ]
+            )
             self.decoder_output_layer = TransformerDecoderOutputLayer(
                 args, embed_tokens, dictionary
             )
@@ -634,7 +652,11 @@ class TransformerDecoder(FairseqDecoder):
             )
             if TORCH_PIPE:
                 self.model = Pipe(
-                    module=partition_model(nn.Sequential(*decoder_module_list), decoder_balance, decoder_devices),
+                    module=partition_model(
+                        nn.Sequential(*decoder_module_list),
+                        decoder_balance,
+                        decoder_devices,
+                    ),
                     chunks=args.pipeline_chunks,
                     checkpoint=args.pipeline_checkpoint,
                 )

fairseq/model_parallel/models/transformer_lm.py

@@ -4,11 +4,11 @@
 # LICENSE file in the root directory of this source tree.
 
 import torch.nn as nn
+
 from fairseq.model_parallel.models.transformer import ModelParallelTransformerDecoder
 from fairseq.models import register_model, register_model_architecture
 from fairseq.models.transformer_lm import TransformerLanguageModel
 
-
 try:
     from fairseq.model_parallel.megatron.mpu import VocabParallelEmbedding
 
@@ -22,7 +22,6 @@ DEFAULT_MAX_TARGET_POSITIONS = 1024
 
 @register_model("model_parallel_transformer_lm")
 class ModelParallelTransformerLanguageModel(TransformerLanguageModel):
-
     @staticmethod
     def add_args(parser):
         TransformerLanguageModel.add_args(parser)
@@ -72,10 +71,6 @@ class ModelParallelTransformerLanguageModel(TransformerLanguageModel):
         )
         return cls(decoder)
 
-    @staticmethod
-    def add_args(parser):
-        TransformerLanguageModel.add_args(parser)
-
     @classmethod
     def build_embedding(cls, args, dictionary, embed_dim, path=None):
         def _vocab_init(tensor, **kwargs):

fairseq/models/__init__.py

@@ -98,9 +98,7 @@ def build_model(cfg: FairseqDataclass, task):
 
     assert model is not None, (
         f"Could not infer model type from {cfg}. "
-        "Available models: {}".format(
-            MODEL_DATACLASS_REGISTRY.keys()
-        )
+        "Available models: {}".format(MODEL_DATACLASS_REGISTRY.keys())
         + f" Requested model type: {model_type}"
     )
 

fairseq/models/bart/hub_interface.py

@@ -100,8 +100,8 @@ class BARTHubInterface(GeneratorHubInterface):
             raise NotImplementedError("prefix generation not implemented for BART")
         res = []
         for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
-            src_tokens = batch['net_input']['src_tokens']
-            inference_step_args["prefix_tokens"] =src_tokens.new_full(
+            src_tokens = batch["net_input"]["src_tokens"]
+            inference_step_args["prefix_tokens"] = src_tokens.new_full(
                 (src_tokens.size(0), 1), fill_value=self.task.source_dictionary.bos()
             ).to(device=self.device)
             results = super().generate(
@@ -111,7 +111,7 @@ class BARTHubInterface(GeneratorHubInterface):
                 skip_invalid_size_inputs=skip_invalid_size_inputs,
                 **kwargs
             )
-            for id, hypos in zip(batch['id'].tolist(), results):
+            for id, hypos in zip(batch["id"].tolist(), results):
                 res.append((id, hypos))
         res = [hypos for _, hypos in sorted(res, key=lambda x: x[0])]
         return res
@@ -177,32 +177,35 @@ class BARTHubInterface(GeneratorHubInterface):
         match_source_len: bool = True,
         **generate_kwargs
     ):
-        masked_token = '<mask>'
+        masked_token = "<mask>"
         batch_tokens = []
         for masked_input in masked_inputs:
-            assert masked_token in masked_input, \
-                "please add one {} token for the input".format(masked_token)
+            assert (
+                masked_token in masked_input
+            ), "please add one {} token for the input".format(masked_token)
 
             text_spans = masked_input.split(masked_token)
-            text_spans_bpe = (' {0} '.format(masked_token)).join(
-                [self.bpe.encode(text_span.rstrip()) for text_span in text_spans]
-            ).strip()
+            text_spans_bpe = (
+                (" {0} ".format(masked_token))
+                .join([self.bpe.encode(text_span.rstrip()) for text_span in text_spans])
+                .strip()
+            )
             tokens = self.task.source_dictionary.encode_line(
-                '<s> ' + text_spans_bpe + ' </s>',
+                "<s> " + text_spans_bpe + " </s>",
                 append_eos=False,
                 add_if_not_exist=False,
             ).long()
             batch_tokens.append(tokens)
 
         # ensure beam size is at least as big as topk
-        generate_kwargs['beam'] = max(
+        generate_kwargs["beam"] = max(
             topk,
-            generate_kwargs.get('beam', -1),
+            generate_kwargs.get("beam", -1),
         )
-        generate_kwargs['match_source_len'] = match_source_len
+        generate_kwargs["match_source_len"] = match_source_len
         batch_hypos = self.generate(batch_tokens, **generate_kwargs)
 
         return [
-            [(self.decode(hypo['tokens']), hypo['score']) for hypo in hypos[:topk]]
+            [(self.decode(hypo["tokens"]), hypo["score"]) for hypo in hypos[:topk]]
             for hypos in batch_hypos
         ]

fairseq/models/bart/model.py

@@ -90,7 +90,7 @@ class BARTModel(TransformerModel):
             src_tokens,
             src_lengths=src_lengths,
             token_embeddings=token_embeddings,
-            return_all_hiddens=return_all_hiddens
+            return_all_hiddens=return_all_hiddens,
         )
         x, extra = self.decoder(
             prev_output_tokens,
@@ -103,9 +103,9 @@ class BARTModel(TransformerModel):
         )
         eos: int = self.eos
         if classification_head_name is not None:
-            sentence_representation = x[
-                src_tokens.eq(eos), :
-            ].view(x.size(0), -1, x.size(-1))[:, -1, :]
+            sentence_representation = x[src_tokens.eq(eos), :].view(
+                x.size(0), -1, x.size(-1)
+            )[:, -1, :]
             for k, head in self.classification_heads.items():
                 # for torch script only supports iteration
                 if k == classification_head_name:

fairseq/models/distributed_fairseq_model.py

@@ -25,7 +25,10 @@ logger = logging.getLogger(__name__)
 
 _SLOWMO_DDP_DISABLED = False
 try:
-    from fairscale.experimental.nn.data_parallel import SlowMoBaseAlgorithm, SlowMoDistributedDataParallel
+    from fairscale.experimental.nn.data_parallel import (
+        SlowMoBaseAlgorithm,
+        SlowMoDistributedDataParallel,
+    )
 except ImportError:
     _SLOWMO_DDP_DISABLED = True
 

fairseq/models/ema/ema.py

@@ -22,6 +22,7 @@ import copy
 import logging
 
 import torch
+
 from fairseq import checkpoint_utils
 
 
@@ -78,7 +79,9 @@ class EMA(object):
         self.fp32_params = {}
 
         if self.config.ema_seed_model is not None:
-            state = checkpoint_utils.load_ema_from_checkpoint(self.config.ema_seed_model)
+            state = checkpoint_utils.load_ema_from_checkpoint(
+                self.config.ema_seed_model
+            )
             self.model.load_state_dict(state["model"], strict=True)
 
         if device is not None:
@@ -119,7 +122,7 @@ class EMA(object):
                 self.fp32_params[param_key] = _to_float(state_dict[param_key])
 
     def restore(self, state_dict, build_fp32_params=False):
-        """ Load data from a model spec into EMA model """
+        """Load data from a model spec into EMA model"""
         self.model.load_state_dict(state_dict, strict=False)
         if build_fp32_params:
             self.build_fp32_params(state_dict)
@@ -131,16 +134,20 @@ class EMA(object):
         return self.decay
 
     def _step_internal(self, new_model, updates=None):
-        """ One update of the EMA model based on new model weights """
+        """One update of the EMA model based on new model weights"""
         decay = self.decay
 
         ema_state_dict = {}
-        ema_params = self.fp32_params if self.config.ema_fp32 else self.model.state_dict()
+        ema_params = (
+            self.fp32_params if self.config.ema_fp32 else self.model.state_dict()
+        )
         for key, param in new_model.state_dict().items():
             try:
                 ema_param = ema_params[key]
             except KeyError:
-                ema_param = param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
+                ema_param = (
+                    param.float().clone() if param.ndim == 1 else copy.deepcopy(param)
+                )
 
             if param.shape != ema_param.shape:
                 raise ValueError(
@@ -151,7 +158,7 @@ class EMA(object):
                 # Do not decay a model.version pytorch param
                 continue
             ema_param.mul_(decay)
-            ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1-decay)
+            ema_param.add_(param.to(dtype=ema_param.dtype), alpha=1 - decay)
             ema_state_dict[key] = ema_param
         self.restore(ema_state_dict, build_fp32_params=False)
 
@@ -168,8 +175,7 @@ class EMA(object):
         """
         self._set_decay(
             0
-            if updates is not None
-            and updates < self.config.ema_start_update
+            if updates is not None and updates < self.config.ema_start_update
             else self.config.ema_decay
         )
         if updates is not None and self.config.ema_update_freq > 1:

fairseq/models/fairseq_decoder.py

@@ -19,7 +19,6 @@ class FairseqDecoder(nn.Module):
         self.onnx_trace = False
         self.adaptive_softmax = None
 
-
     def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
         """
         Args:

fairseq/models/fairseq_model.py

@@ -29,8 +29,9 @@ logger = logging.getLogger(__name__)
 
 def check_type(module, expected_type):
     if hasattr(module, "unwrapped_module"):
-        assert isinstance(module.unwrapped_module, expected_type), \
-            f"{type(module.unwrapped_module)} != {expected_type}"
+        assert isinstance(
+            module.unwrapped_module, expected_type
+        ), f"{type(module.unwrapped_module)} != {expected_type}"
     else:
         assert isinstance(module, expected_type), f"{type(module)} != {expected_type}"
 
@@ -114,7 +115,9 @@ class BaseFairseqModel(nn.Module):
         """
 
         if model_cfg is None and args is not None:
-            logger.warn("using 'args' is deprecated, please update your code to use dataclass config")
+            logger.warn(
+                "using 'args' is deprecated, please update your code to use dataclass config"
+            )
             model_cfg = convert_namespace_to_omegaconf(args).model
 
         self.upgrade_state_dict(state_dict)
@@ -454,7 +457,9 @@ class FairseqMultiModel(BaseFairseqModel):
         """
 
         if model_cfg is None and args is not None:
-            logger.warn("using 'args' is deprecated, please update your code to use dataclass config")
+            logger.warn(
+                "using 'args' is deprecated, please update your code to use dataclass config"
+            )
             model_cfg = convert_namespace_to_omegaconf(args).model
 
         self.upgrade_state_dict(state_dict)

fairseq/models/hubert/hubert.py

@@ -30,9 +30,7 @@ from omegaconf import II
 logger = logging.getLogger(__name__)
 
 EXTRACTOR_MODE_CHOICES = ChoiceEnum(["default", "layer_norm"])
-MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(
-    ["static", "uniform", "normal", "poisson"]
-)
+MASKING_DISTRIBUTION_CHOICES = ChoiceEnum(["static", "uniform", "normal", "poisson"])
 
 
 @dataclass
@@ -86,9 +84,7 @@ class HubertConfig(FairseqDataclass):
     )
     dropout_features: float = field(
         default=0.0,
-        metadata={
-            "help": "dropout to apply to the features (after feat extr)"
-        },
+        metadata={"help": "dropout to apply to the features (after feat extr)"},
     )
 
     final_dim: int = field(
@@ -150,9 +146,7 @@ class HubertConfig(FairseqDataclass):
     )
     mask_min_space: int = field(
         default=1,
-        metadata={
-            "help": "min space between spans (if no overlap is enabled)"
-        },
+        metadata={"help": "min space between spans (if no overlap is enabled)"},
     )
 
     # channel masking
@@ -182,23 +176,17 @@ class HubertConfig(FairseqDataclass):
     )
     mask_channel_min_space: int = field(
         default=1,
-        metadata={
-            "help": "min space between spans (if no overlap is enabled)"
-        },
+        metadata={"help": "min space between spans (if no overlap is enabled)"},
     )
 
     # positional embeddings
     conv_pos: int = field(
         default=128,
-        metadata={
-            "help": "number of filters for convolutional positional embeddings"
-        },
+        metadata={"help": "number of filters for convolutional positional embeddings"},
     )
     conv_pos_groups: int = field(
         default=16,
-        metadata={
-            "help": "number of groups for convolutional positional embedding"
-        },
+        metadata={"help": "number of groups for convolutional positional embedding"},
     )
 
     latent_temp: Tuple[float, float, float] = field(
@@ -238,9 +226,7 @@ class HubertModel(BaseFairseqModel):
             conv_bias=cfg.conv_bias,
         )
         feature_ds_rate = np.prod([s for _, _, s in feature_enc_layers])
-        self.feat2tar_ratio = (
-            cfg.label_rate * feature_ds_rate / task_cfg.sample_rate
-        )
+        self.feat2tar_ratio = cfg.label_rate * feature_ds_rate / task_cfg.sample_rate
 
         self.post_extract_proj = (
             nn.Linear(self.embed, cfg.encoder_embed_dim)
@@ -270,9 +256,7 @@ class HubertModel(BaseFairseqModel):
         self.skip_masked = cfg.skip_masked
         self.skip_nomask = cfg.skip_nomask
 
-        final_dim = (
-            cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
-        )
+        final_dim = cfg.final_dim if cfg.final_dim > 0 else cfg.encoder_embed_dim
 
         self.mask_emb = nn.Parameter(
             torch.FloatTensor(cfg.encoder_embed_dim).uniform_()
@@ -297,9 +281,7 @@ class HubertModel(BaseFairseqModel):
 
         # modules below are not needed during fine-tuning
         if any([d is None for d in dictionaries]):
-            logger.info(
-                "cannot find dictionary. assume will be used for fine-tuning"
-            )
+            logger.info("cannot find dictionary. assume will be used for fine-tuning")
         else:
             self.num_classes = [len(d) for d in dictionaries]
             self.label_embs_concat = nn.Parameter(
@@ -365,9 +347,7 @@ class HubertModel(BaseFairseqModel):
         pos = pos.unsqueeze(0)
         targets = torch.cat([pos, negs], dim=0)
 
-        logits = torch.cosine_similarity(
-            x.float(), targets.float(), dim=-1
-        ).type_as(x)
+        logits = torch.cosine_similarity(x.float(), targets.float(), dim=-1).type_as(x)
         logits /= self.logit_temp
         if neg_is_pos.any():
             logits[1:][neg_is_pos] = float("-inf")
@@ -385,7 +365,9 @@ class HubertModel(BaseFairseqModel):
         return features
 
     def forward_targets(
-        self, features: torch.Tensor, target_list: List[torch.Tensor],
+        self,
+        features: torch.Tensor,
+        target_list: List[torch.Tensor],
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         # Trim features to ensure labels exist and then get aligned labels
         feat_tsz = features.size(2)
@@ -398,14 +380,14 @@ class HubertModel(BaseFairseqModel):
         return features, target_list
 
     def forward_padding_mask(
-        self, features: torch.Tensor, padding_mask: torch.Tensor,
+        self,
+        features: torch.Tensor,
+        padding_mask: torch.Tensor,
     ) -> torch.Tensor:
         extra = padding_mask.size(1) % features.size(1)
         if extra > 0:
             padding_mask = padding_mask[:, :-extra]
-        padding_mask = padding_mask.view(
-            padding_mask.size(0), features.size(1), -1
-        )
+        padding_mask = padding_mask.view(padding_mask.size(0), features.size(1), -1)
         padding_mask = padding_mask.all(-1)
         return padding_mask
 
@@ -439,9 +421,7 @@ class HubertModel(BaseFairseqModel):
         unmasked_features = self.dropout_features(unmasked_features)
 
         if mask:
-            x, mask_indices = self.apply_mask(
-                features, padding_mask, target_list
-            )
+            x, mask_indices = self.apply_mask(features, padding_mask, target_list)
         else:
             x = features
             mask_indices = None
@@ -454,7 +434,7 @@ class HubertModel(BaseFairseqModel):
         x, _ = self.encoder(
             x,
             padding_mask=padding_mask,
-            layer=None if output_layer is None else output_layer - 1
+            layer=None if output_layer is None else output_layer - 1,
         )
 
         if features_only:
@@ -483,9 +463,7 @@ class HubertModel(BaseFairseqModel):
                 proj_x_m_list = [proj_x_m for _ in range(len(target_list))]
             logit_m_list = [
                 compute_pred(proj_x_m, t[masked_indices], label_embs_list[i])
-                for i, (proj_x_m, t) in enumerate(
-                    zip(proj_x_m_list, target_list)
-                )
+                for i, (proj_x_m, t) in enumerate(zip(proj_x_m_list, target_list))
             ]
         else:
             logit_m_list = [None for _ in target_list]
@@ -500,9 +478,7 @@ class HubertModel(BaseFairseqModel):
 
             logit_u_list = [
                 compute_pred(proj_x_u, t[nomask_indices], label_embs_list[i])
-                for i, (proj_x_u, t) in enumerate(
-                    zip(proj_x_u_list, target_list)
-                )
+                for i, (proj_x_u, t) in enumerate(zip(proj_x_u_list, target_list))
             ]
         else:
             logit_u_list = [None for _ in target_list]
@@ -543,9 +519,7 @@ class HubertModel(BaseFairseqModel):
 
     def get_targets(self, net_output, is_masked=True):
         logits_list = self.get_logits(net_output, is_masked)
-        targets_list = [
-            x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list
-        ]
+        targets_list = [x.new_zeros(x.size(0), dtype=torch.long) for x in logits_list]
         return targets_list
 
     def get_extra_losses(self, net_output):

fairseq/models/hubert/hubert_asr.py

@@ -21,9 +21,7 @@ from omegaconf import II, MISSING
 
 @dataclass
 class HubertAsrConfig(FairseqDataclass):
-    w2v_path: str = field(
-        default=MISSING, metadata={"help": "path to hubert model"}
-    )
+    w2v_path: str = field(default=MISSING, metadata={"help": "path to hubert model"})
     no_pretrained_weights: bool = field(
         default=False,
         metadata={"help": "if true, does not load pretrained weights"},
@@ -34,9 +32,7 @@ class HubertAsrConfig(FairseqDataclass):
     )
     final_dropout: float = field(
         default=0.0,
-        metadata={
-            "help": "dropout after transformer and before final projection"
-        },
+        metadata={"help": "dropout after transformer and before final projection"},
     )
     dropout: float = field(
         default=0.0,
@@ -45,15 +41,13 @@ class HubertAsrConfig(FairseqDataclass):
     attention_dropout: float = field(
         default=0.0,
         metadata={
-            "help": "dropout probability for attention weights "
-            "inside hubert model"
+            "help": "dropout probability for attention weights " "inside hubert model"
         },
     )
     activation_dropout: float = field(
         default=0.0,
         metadata={
-            "help": "dropout probability after activation in FFN "
-            "inside hubert model"
+            "help": "dropout probability after activation in FFN " "inside hubert model"
         },
     )
 
@@ -184,9 +178,7 @@ class HubertSeq2SeqConfig(HubertAsrConfig):
     decoder_ffn_embed_dim: int = field(
         default=3072, metadata={"help": "decoder embedding dimension for FFN"}
     )
-    decoder_layers: int = field(
-        default=6, metadata={"help": "num of decoder layers"}
-    )
+    decoder_layers: int = field(default=6, metadata={"help": "num of decoder layers"})
     decoder_layerdrop: float = field(
         default=0.0, metadata={"help": "decoder layerdrop chance"}
     )
@@ -204,8 +196,7 @@ class HubertSeq2SeqConfig(HubertAsrConfig):
     no_token_positional_embeddings: bool = field(
         default=False,
         metadata={
-            "help": "if set, disables positional embeddings "
-            "(outside self attention)"
+            "help": "if set, disables positional embeddings " "(outside self attention)"
         },
     )
     decoder_dropout: float = field(
@@ -214,15 +205,13 @@ class HubertSeq2SeqConfig(HubertAsrConfig):
     decoder_attention_dropout: float = field(
         default=0.0,
         metadata={
-            "help": "dropout probability for attention weights "
-            "inside the decoder"
+            "help": "dropout probability for attention weights " "inside the decoder"
         },
     )
     decoder_activation_dropout: float = field(
         default=0.0,
         metadata={
-            "help": "dropout probability after activation in FFN "
-            "inside the decoder"
+            "help": "dropout probability after activation in FFN " "inside the decoder"
         },
     )
     max_target_positions: int = field(
@@ -258,9 +247,7 @@ class HubertEncoder(FairseqEncoder):
         }
 
         if cfg.w2v_args is None:
-            state = checkpoint_utils.load_checkpoint_to_cpu(
-                cfg.w2v_path, arg_overrides
-            )
+            state = checkpoint_utils.load_checkpoint_to_cpu(cfg.w2v_path, arg_overrides)
             w2v_args = state.get("cfg", None)
             if w2v_args is None:
                 w2v_args = convert_namespace_to_omegaconf(state["args"])
@@ -269,9 +256,7 @@ class HubertEncoder(FairseqEncoder):
             state = None
             w2v_args = cfg.w2v_args
             if isinstance(w2v_args, Namespace):
-                cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(
-                    w2v_args
-                )
+                cfg.w2v_args = w2v_args = convert_namespace_to_omegaconf(w2v_args)
 
         assert cfg.normalize == w2v_args.task.normalize, (
             "Fine-tuning works best when data normalization is the same. "
@@ -344,9 +329,9 @@ class HubertEncoder(FairseqEncoder):
 
     def reorder_encoder_out(self, encoder_out, new_order):
         if encoder_out["encoder_out"] is not None:
-            encoder_out["encoder_out"] = encoder_out[
-                "encoder_out"
-            ].index_select(1, new_order)
+            encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
+                1, new_order
+            )
         if encoder_out["encoder_padding_mask"] is not None:
             encoder_out["encoder_padding_mask"] = encoder_out[
                 "encoder_padding_mask"

fairseq/models/lstm.py

@@ -225,10 +225,10 @@ class LSTMEncoder(FairseqEncoder):
         super().__init__(dictionary)
         self.num_layers = num_layers
         self.dropout_in_module = FairseqDropout(
-            dropout_in*1.0, module_name=self.__class__.__name__
+            dropout_in * 1.0, module_name=self.__class__.__name__
         )
         self.dropout_out_module = FairseqDropout(
-            dropout_out*1.0, module_name=self.__class__.__name__
+            dropout_out * 1.0, module_name=self.__class__.__name__
         )
         self.bidirectional = bidirectional
         self.hidden_size = hidden_size
@@ -329,7 +329,9 @@ class LSTMEncoder(FairseqEncoder):
         out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()
         return out.view(self.num_layers, bsz, -1)
 
-    def reorder_encoder_out(self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order):
+    def reorder_encoder_out(
+        self, encoder_out: Tuple[Tensor, Tensor, Tensor, Tensor], new_order
+    ):
         return tuple(
             (
                 encoder_out[0].index_select(1, new_order),
@@ -402,10 +404,10 @@ class LSTMDecoder(FairseqIncrementalDecoder):
     ):
         super().__init__(dictionary)
         self.dropout_in_module = FairseqDropout(
-            dropout_in*1.0, module_name=self.__class__.__name__
+            dropout_in * 1.0, module_name=self.__class__.__name__
         )
         self.dropout_out_module = FairseqDropout(
-            dropout_out*1.0, module_name=self.__class__.__name__
+            dropout_out * 1.0, module_name=self.__class__.__name__
         )
         self.hidden_size = hidden_size
         self.share_input_output_embed = share_input_output_embed

fairseq/models/nat/fairseq_nat_model.py

@@ -18,7 +18,10 @@ def ensemble_encoder(func):
     def wrapper(self, *args, **kwargs):
         if self.ensemble_models is None or len(self.ensemble_models) == 1:
             return func(self, *args, **kwargs)
-        encoder_outs = [func(model, *args, **kwargs, return_all_hiddens=True) for model in self.ensemble_models]
+        encoder_outs = [
+            func(model, *args, **kwargs, return_all_hiddens=True)
+            for model in self.ensemble_models
+        ]
         _encoder_out = encoder_outs[0].copy()
 
         def stack(key):
@@ -56,8 +59,7 @@ def ensemble_decoder(func):
                 model,
                 normalize=normalize,
                 encoder_out=_replace(
-                    encoder_out,
-                    encoder_out["encoder_out"][0][:, :, :, i]
+                    encoder_out, encoder_out["encoder_out"][0][:, :, :, i]
                 ),
                 *args,
                 **kwargs

fairseq/models/nat/nonautoregressive_ensembles.py

@@ -85,7 +85,8 @@ class EnsembleLevT(BasicEnsembleModel):
         else:
             if not encoder_outs[0]["encoder_padding_mask"]:
                 src_lens = (
-                    encoder_outs[0]["encoder_out"][0].new(bsz)
+                    encoder_outs[0]["encoder_out"][0]
+                    .new(bsz)
                     .fill_(encoder_outs[0]["encoder_out"][0].size(1))
                 )
             else:

fairseq/models/roberta/model.py

@@ -183,7 +183,7 @@ class RobertaModel(FairseqEncoderModel):
                 "communication less efficient due to smaller input sizes. This option "
                 "is set to 0 (i.e., always wrap) when --checkpoint-activations or "
                 "--offload-activations are passed."
-            )
+            ),
         )
 
     @classmethod
@@ -542,7 +542,9 @@ def base_architecture(args):
     args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", True)
     args.no_scale_embedding = safe_getattr(args, "no_scale_embedding", True)
     args.activation_fn = safe_getattr(args, "activation_fn", "gelu")
-    args.encoder_normalize_before = safe_getattr(args, "encoder_normalize_before", False)
+    args.encoder_normalize_before = safe_getattr(
+        args, "encoder_normalize_before", False
+    )
     args.pooler_activation_fn = safe_getattr(args, "pooler_activation_fn", "tanh")
     args.untie_weights_roberta = safe_getattr(args, "untie_weights_roberta", False)
 

fairseq/models/roberta/model_gottbert.py

@@ -12,26 +12,26 @@ from .hub_interface import RobertaHubInterface
 from .model import RobertaModel
 
 
-@register_model('gottbert')
+@register_model("gottbert")
 class GottbertModel(RobertaModel):
-
     @classmethod
     def hub_models(cls):
         return {
-            'gottbert-base': 'https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz',
+            "gottbert-base": "https://dl.gottbert.de/fairseq/models/gottbert-base.tar.gz",
         }
 
     @classmethod
-    def from_pretrained(cls,
-                        model_name_or_path,
-                        checkpoint_file='model.pt',
-                        data_name_or_path='.',
-                        bpe='hf_byte_bpe',
-                        bpe_vocab='vocab.json',
-                        bpe_merges='merges.txt',
-                        bpe_add_prefix_space=False,
-                        **kwargs
-                        ):
+    def from_pretrained(
+        cls,
+        model_name_or_path,
+        checkpoint_file="model.pt",
+        data_name_or_path=".",
+        bpe="hf_byte_bpe",
+        bpe_vocab="vocab.json",
+        bpe_merges="merges.txt",
+        bpe_add_prefix_space=False,
+        **kwargs
+    ):
         from fairseq import hub_utils
 
         x = hub_utils.from_pretrained(
@@ -46,4 +46,4 @@ class GottbertModel(RobertaModel):
             bpe_add_prefix_space=bpe_add_prefix_space,
             **kwargs,
         )
-        return RobertaHubInterface(x['args'], x['task'], x['models'][0])
+        return RobertaHubInterface(x["args"], x["task"], x["models"][0])

fairseq/models/speech_to_text/s2t_transformer.py

@@ -202,10 +202,10 @@ class S2TTransformerModel(FairseqEncoderDecoderModel):
             help="model to take encoder weights from (for initialization)",
         )
         parser.add_argument(
-            '--encoder-freezing-updates',
+            "--encoder-freezing-updates",
             type=int,
-            metavar='N',
-            help='freeze encoder for first N updates'
+            metavar="N",
+            help="freeze encoder for first N updates",
         )
 
     @classmethod
@@ -329,7 +329,9 @@ class S2TTransformerEncoder(FairseqEncoder):
 
         return {
             "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask] if encoder_padding_mask.any() else [],  # B x T
+            "encoder_padding_mask": [encoder_padding_mask]
+            if encoder_padding_mask.any()
+            else [],  # B x T
             "encoder_embedding": [],  # B x T x C
             "encoder_states": encoder_states,  # List[T x B x C]
             "src_tokens": [],
@@ -339,27 +341,37 @@ class S2TTransformerEncoder(FairseqEncoder):
     def forward(self, src_tokens, src_lengths, return_all_hiddens=False):
         if self.num_updates < self.encoder_freezing_updates:
             with torch.no_grad():
-                x = self._forward(src_tokens, src_lengths,
-                                  return_all_hiddens=return_all_hiddens)
+                x = self._forward(
+                    src_tokens, src_lengths, return_all_hiddens=return_all_hiddens
+                )
         else:
-            x = self._forward(src_tokens, src_lengths,
-                              return_all_hiddens=return_all_hiddens)
+            x = self._forward(
+                src_tokens, src_lengths, return_all_hiddens=return_all_hiddens
+            )
         return x
 
     def reorder_encoder_out(self, encoder_out, new_order):
         new_encoder_out = (
-            [] if len(encoder_out["encoder_out"]) == 0
+            []
+            if len(encoder_out["encoder_out"]) == 0
             else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
         )
 
         new_encoder_padding_mask = (
-            [] if len(encoder_out["encoder_padding_mask"]) == 0
-            else [x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]]
+            []
+            if len(encoder_out["encoder_padding_mask"]) == 0
+            else [
+                x.index_select(0, new_order)
+                for x in encoder_out["encoder_padding_mask"]
+            ]
         )
 
         new_encoder_embedding = (
-            [] if len(encoder_out["encoder_embedding"]) == 0
-            else [x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]]
+            []
+            if len(encoder_out["encoder_embedding"]) == 0
+            else [
+                x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
+            ]
         )
 
         encoder_states = encoder_out["encoder_states"]

fairseq/models/speech_to_text/xm_transformer.py

@@ -9,8 +9,12 @@ import copy
 from typing import Dict, List, Optional, Tuple
 
 from fairseq import utils, checkpoint_utils
-from fairseq.models import (FairseqEncoderDecoderModel, FairseqEncoder,
-                            register_model, register_model_architecture)
+from fairseq.models import (
+    FairseqEncoderDecoderModel,
+    FairseqEncoder,
+    register_model,
+    register_model_architecture,
+)
 from fairseq.models.transformer import Embedding, TransformerDecoder
 from fairseq.models.wav2vec import Wav2VecEncoder
 from fairseq.modules.layer_norm import LayerNorm
@@ -24,18 +28,23 @@ logger = logging.getLogger(__name__)
 
 
 class Conv1dAdaptor(nn.Module):
-    def __init__(self, in_dim, out_dim, n_layers=3, kernel_size=3, stride=2,
-                 add_layernorm=False):
+    def __init__(
+        self, in_dim, out_dim, n_layers=3, kernel_size=3, stride=2, add_layernorm=False
+    ):
         super().__init__()
         self.layers = nn.ModuleList(
-            nn.Conv1d(in_dim if i == 0 else out_dim, out_dim * 2, kernel_size,
-                      stride=stride, padding=kernel_size // 2)
+            nn.Conv1d(
+                in_dim if i == 0 else out_dim,
+                out_dim * 2,
+                kernel_size,
+                stride=stride,
+                padding=kernel_size // 2,
+            )
             for i in range(n_layers)
         )
         self.layernorms = None
         if add_layernorm:
-            self.layernorms = nn.ModuleList(LayerNorm(out_dim)
-                                            for _ in range(n_layers))
+            self.layernorms = nn.ModuleList(LayerNorm(out_dim) for _ in range(n_layers))
         self.stride = stride
 
     @classmethod
@@ -43,7 +52,7 @@ class Conv1dAdaptor(nn.Module):
         parser.add_argument("--adaptor-n-layers", type=int)
         parser.add_argument("--adaptor-kernel-size", type=int)
         parser.add_argument("--adaptor-stride", type=int)
-        parser.add_argument("--adaptor-layernorm", action='store_true')
+        parser.add_argument("--adaptor-layernorm", action="store_true")
 
     def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
         out = in_seq_lens_tensor.clone()
@@ -197,15 +206,18 @@ class Wav2VecEncoderWithAdaptor(FairseqEncoder):
         encoder_out_dim = self.w2v_encoder.w2v_model.encoder.embedding_dim
         # Projection + 8x shrinking
         self.adaptor = Conv1dAdaptor(
-            encoder_out_dim, args.decoder_embed_dim,
+            encoder_out_dim,
+            args.decoder_embed_dim,
             n_layers=args.adaptor_n_layers,
-            kernel_size=args.adaptor_kernel_size, stride=args.adaptor_stride,
-            add_layernorm=args.adaptor_layernorm
+            kernel_size=args.adaptor_kernel_size,
+            stride=args.adaptor_stride,
+            add_layernorm=args.adaptor_layernorm,
         )
         for k, p in self.w2v_encoder.w2v_model.named_parameters():
             # Freeze pretrained models by default
-            if safe_hasattr(args, 'finetune_w2v_params') and XMTransformerModel.finetune_params(
-                    args.finetune_w2v_params, k):
+            if safe_hasattr(
+                args, "finetune_w2v_params"
+            ) and XMTransformerModel.finetune_params(args.finetune_w2v_params, k):
                 p.requires_grad = True
             else:
                 p.requires_grad = False
@@ -214,11 +226,16 @@ class Wav2VecEncoderWithAdaptor(FairseqEncoder):
     def add_args(cls, parser):
         add_wav2vec_asr_args(parser)
         parser.add_argument(
-            "--normalize", action="store_true",
+            "--normalize",
+            action="store_true",
             help="if set, normalizes input to have 0 mean and unit variance",
         )
-        parser.add_argument("--finetune-w2v-params", type=str, metavar="STR",
-                            help="comma-separated param strings to finetune.")
+        parser.add_argument(
+            "--finetune-w2v-params",
+            type=str,
+            metavar="STR",
+            help="comma-separated param strings to finetune.",
+        )
         Conv1dAdaptor.add_args(parser)
 
     def forward(self, src_tokens, src_lengths=None, **kwargs):
@@ -227,13 +244,17 @@ class Wav2VecEncoderWithAdaptor(FairseqEncoder):
         x = out["encoder_out"]
         enc_padding_mask = None
         if out["encoder_padding_mask"] is not None:
-            enc_padding_mask = out["encoder_padding_mask"].transpose(0, 1)   # T X B --> B X T
+            enc_padding_mask = out["encoder_padding_mask"].transpose(
+                0, 1
+            )  # T X B --> B X T
 
         x, enc_padding_mask = self.adaptor(x, enc_padding_mask)
 
         return {
             "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [enc_padding_mask] if enc_padding_mask.any() else [],  # B x T
+            "encoder_padding_mask": [enc_padding_mask]
+            if enc_padding_mask.any()
+            else [],  # B x T
             "encoder_embedding": [],  # B x T x C
             "encoder_states": [],  # List[T x B x C]
             "src_tokens": [],
@@ -242,20 +263,26 @@ class Wav2VecEncoderWithAdaptor(FairseqEncoder):
 
     def reorder_encoder_out(self, encoder_out, new_order):
         new_encoder_out = (
-            [] if len(encoder_out["encoder_out"]) == 0
+            []
+            if len(encoder_out["encoder_out"]) == 0
             else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
         )
 
         new_encoder_padding_mask = (
-            [] if len(encoder_out["encoder_padding_mask"]) == 0
-            else [x.index_select(0, new_order) for x in
-                  encoder_out["encoder_padding_mask"]]
+            []
+            if len(encoder_out["encoder_padding_mask"]) == 0
+            else [
+                x.index_select(0, new_order)
+                for x in encoder_out["encoder_padding_mask"]
+            ]
         )
 
         new_encoder_embedding = (
-            [] if len(encoder_out["encoder_embedding"]) == 0
-            else [x.index_select(0, new_order) for x in
-                  encoder_out["encoder_embedding"]]
+            []
+            if len(encoder_out["encoder_embedding"]) == 0
+            else [
+                x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
+            ]
         )
 
         encoder_states = encoder_out["encoder_states"]
@@ -274,38 +301,71 @@ class Wav2VecEncoderWithAdaptor(FairseqEncoder):
 
 
 def add_decoder_args(parser):
-    parser.add_argument("--activation-fn", type=str, default='relu',
-                        choices=utils.get_available_activation_fns(),
-                        help="activation function to use")
-    parser.add_argument("--decoder-dropout", type=float, metavar="D",
-                        help="dropout probability")
-    parser.add_argument("--decoder-attention-dropout", type=float,
-                        metavar="D",
-                        help="dropout probability for attention weights")
-    parser.add_argument("--decoder-activation-dropout", type=float,
-                        metavar="D",
-                        help="dropout probability after activation in FFN.")
-    parser.add_argument("--decoder-embed-dim", type=int, metavar="N",
-                        help="decoder embedding dimension")
-    parser.add_argument("--decoder-ffn-embed-dim", type=int, metavar="N",
-                        help="decoder embedding dimension for FFN")
-    parser.add_argument("--decoder-layers", type=int, metavar="N",
-                        help="num decoder layers")
-    parser.add_argument("--decoder-attention-heads", type=int, metavar="N",
-                        help="num decoder attention heads")
-    parser.add_argument("--decoder-normalize-before", action="store_true",
-                        help="apply layernorm before each decoder block")
-    parser.add_argument("--layernorm-embedding", action="store_true",
-                        help="add layernorm to embedding")
-    parser.add_argument("--no-scale-embedding", action="store_true",
-                        help="if True, dont scale embeddings")
     parser.add_argument(
-        "--load-pretrained-decoder-from", type=str, metavar="STR",
-        help="model to take decoder weights from (for initialization)"
+        "--activation-fn",
+        type=str,
+        default="relu",
+        choices=utils.get_available_activation_fns(),
+        help="activation function to use",
+    )
+    parser.add_argument(
+        "--decoder-dropout", type=float, metavar="D", help="dropout probability"
+    )
+    parser.add_argument(
+        "--decoder-attention-dropout",
+        type=float,
+        metavar="D",
+        help="dropout probability for attention weights",
+    )
+    parser.add_argument(
+        "--decoder-activation-dropout",
+        type=float,
+        metavar="D",
+        help="dropout probability after activation in FFN.",
+    )
+    parser.add_argument(
+        "--decoder-embed-dim", type=int, metavar="N", help="decoder embedding dimension"
+    )
+    parser.add_argument(
+        "--decoder-ffn-embed-dim",
+        type=int,
+        metavar="N",
+        help="decoder embedding dimension for FFN",
+    )
+    parser.add_argument(
+        "--decoder-layers", type=int, metavar="N", help="num decoder layers"
+    )
+    parser.add_argument(
+        "--decoder-attention-heads",
+        type=int,
+        metavar="N",
+        help="num decoder attention heads",
+    )
+    parser.add_argument(
+        "--decoder-normalize-before",
+        action="store_true",
+        help="apply layernorm before each decoder block",
+    )
+    parser.add_argument(
+        "--layernorm-embedding", action="store_true", help="add layernorm to embedding"
+    )
+    parser.add_argument(
+        "--no-scale-embedding",
+        action="store_true",
+        help="if True, dont scale embeddings",
+    )
+    parser.add_argument(
+        "--load-pretrained-decoder-from",
+        type=str,
+        metavar="STR",
+        help="model to take decoder weights from (for initialization)",
+    )
+    parser.add_argument(
+        "--finetune-decoder-params",
+        type=str,
+        metavar="STR",
+        help="comma-separated param strings to finetune.",
     )
-    parser.add_argument("--finetune-decoder-params", type=str,
-                        metavar="STR",
-                        help="comma-separated param strings to finetune.")
     parser.add_argument("--checkpoint-activations", action="store_true")
 
 
@@ -342,16 +402,16 @@ class XMTransformerModel(FairseqEncoderDecoderModel):
         _args.activation_dropout = args.decoder_activation_dropout
         _args.max_target_positions = 1024
 
-        decoder = TransformerDecoder(_args, task.target_dictionary,
-                                     embed_tokens)
+        decoder = TransformerDecoder(_args, task.target_dictionary, embed_tokens)
         if getattr(args, "load_pretrained_decoder_from", None):
             decoder = checkpoint_utils.load_pretrained_component_from_model(
                 component=decoder, checkpoint=args.load_pretrained_decoder_from
             )
         for k, p in decoder.named_parameters():
             # Freeze pretrained models by default
-            if safe_hasattr(args, 'finetune_decoder_params') and XMTransformerModel.finetune_params(
-                    args.finetune_decoder_params, k):
+            if safe_hasattr(
+                args, "finetune_decoder_params"
+            ) and XMTransformerModel.finetune_params(args.finetune_decoder_params, k):
                 p.requires_grad = True
             else:
                 p.requires_grad = False
@@ -369,8 +429,9 @@ class XMTransformerModel(FairseqEncoderDecoderModel):
             padding_idx = dictionary.pad()
             return Embedding(num_embeddings, embed_dim, padding_idx)
 
-        decoder_embed_tokens = build_embedding(task.target_dictionary,
-                                               args.decoder_embed_dim)
+        decoder_embed_tokens = build_embedding(
+            task.target_dictionary, args.decoder_embed_dim
+        )
         encoder = cls.build_encoder(args)
         decoder = cls.build_decoder(args, task, decoder_embed_tokens)
         return cls(encoder, decoder)
@@ -382,8 +443,7 @@ class XMTransformerModel(FairseqEncoderDecoderModel):
         sample: Optional[Dict[str, Tensor]] = None,
     ):
         # net_output['encoder_out'] is a (B, T, D) tensor
-        lprobs = self.get_normalized_probs_scriptable(net_output, log_probs,
-                                                      sample)
+        lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
         lprobs.batch_first = True
         return lprobs
 
@@ -393,17 +453,19 @@ class XMTransformerModel(FairseqEncoderDecoderModel):
         argument in its input, which is not supported in torchscript. This
         method overrites the forward method definition without **kwargs.
         """
-        encoder_out = self.encoder(src_tokens=src_tokens,
-                                   src_lengths=src_lengths, **kwargs)
-        decoder_out = self.decoder(prev_output_tokens=prev_output_tokens,
-                                   encoder_out=encoder_out)
+        encoder_out = self.encoder(
+            src_tokens=src_tokens, src_lengths=src_lengths, **kwargs
+        )
+        decoder_out = self.decoder(
+            prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
+        )
         return decoder_out
 
     def upgrade_state_dict(self, state_dict):
         for k, _ in state_dict.items():
-            if 'adaptor.layers' in state_dict:
+            if "adaptor.layers" in state_dict:
                 print(k)
-                new = k.replace('adaptor.layers', 'adaptor_layers')
+                new = k.replace("adaptor.layers", "adaptor_layers")
                 state_dict[new] = state_dict[k]
                 del state_dict[k]
 
@@ -435,11 +497,9 @@ def set_default_w2v_encoder_args(args):
     args.mask_channel_length = getattr(args, "mask_channel_length", 10)
     args.mask_channel_prob = getattr(args, "mask_channel_prob", 0.5)
     args.mask_channel_before = getattr(args, "mask_channel_before", False)
-    args.mask_channel_selection = getattr(args, "mask_channel_selection",
-                                          "static")
+    args.mask_channel_selection = getattr(args, "mask_channel_selection", "static")
     args.mask_channel_other = getattr(args, "mask_channel_other", 0)
-    args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap",
-                                           False)
+    args.no_mask_channel_overlap = getattr(args, "no_mask_channel_overlap", False)
 
     args.freeze_finetune_updates = getattr(args, "freeze_finetune_updates", 0)
     args.feature_grad_mult = 0.1
@@ -456,49 +516,43 @@ def set_default_adaptor_args(args):
 
 
 def set_default_mbart_decoder_args(args):
-    args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
-    args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
-    args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim',
-                                         4 * 1024)
-    args.decoder_layers = getattr(args, 'decoder_layers', 12)
-    args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
-    args.decoder_normalize_before = getattr(args, 'decoder_normalize_before',
-                                            True)
-    args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True)
+    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
+    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
+    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * 1024)
+    args.decoder_layers = getattr(args, "decoder_layers", 12)
+    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
+    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
+    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", True)
     args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
     args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.decoder_attention_dropout = getattr(args, 'decoder_attention_dropout',
-                                             0.)
-    args.decoder_activation_dropout = getattr(args,
-                                              'decoder_activation_dropout', 0.)
-    args.decoder_dropout = getattr(args, 'decoder_dropout', 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff',
-                                           None)
-    args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
+    args.decoder_attention_dropout = getattr(args, "decoder_attention_dropout", 0.0)
+    args.decoder_activation_dropout = getattr(args, "decoder_activation_dropout", 0.0)
+    args.decoder_dropout = getattr(args, "decoder_dropout", 0.1)
+    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
+    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
     args.share_decoder_input_output_embed = getattr(
-        args, 'share_decoder_input_output_embed', True
+        args, "share_decoder_input_output_embed", True
     )
     args.no_token_positional_embeddings = getattr(
         args, "no_token_positional_embeddings", False
     )
 
-    args.decoder_output_dim = getattr(args, 'decoder_output_dim',
-                                      args.decoder_embed_dim)
-    args.decoder_input_dim = getattr(args, 'decoder_input_dim',
-                                     args.decoder_embed_dim)
+    args.decoder_output_dim = getattr(
+        args, "decoder_output_dim", args.decoder_embed_dim
+    )
+    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
 
-    args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
+    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
     args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.layernorm_embedding = getattr(args, 'layernorm_embedding', True)
+    args.layernorm_embedding = getattr(args, "layernorm_embedding", True)
 
-    args.activation_fn = getattr(args, 'activation_fn', 'gelu')
-    args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
-    args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
+    args.activation_fn = getattr(args, "activation_fn", "gelu")
+    args.pooler_activation_fn = getattr(args, "pooler_activation_fn", "tanh")
+    args.pooler_dropout = getattr(args, "pooler_dropout", 0.0)
     args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
 
 
-@register_model_architecture(model_name="xm_transformer",
-                             arch_name="xm_transformer")
+@register_model_architecture(model_name="xm_transformer", arch_name="xm_transformer")
 def base_architecture(args):
     set_default_w2v_encoder_args(args)
     set_default_adaptor_args(args)

fairseq/models/text_to_speech/fastspeech2.py

@@ -8,10 +8,17 @@ import logging
 import torch
 from torch import nn
 
-from fairseq.models import (FairseqEncoder, FairseqEncoderModel, register_model,
-                            register_model_architecture)
+from fairseq.models import (
+    FairseqEncoder,
+    FairseqEncoderModel,
+    register_model,
+    register_model_architecture,
+)
 from fairseq.modules import (
-    LayerNorm, PositionalEmbedding, FairseqDropout, MultiheadAttention
+    LayerNorm,
+    PositionalEmbedding,
+    FairseqDropout,
+    MultiheadAttention,
 )
 from fairseq import utils
 from fairseq.data.data_utils import lengths_to_padding_mask
@@ -36,11 +43,19 @@ class PositionwiseFeedForward(nn.Module):
     def __init__(self, in_dim, hidden_dim, kernel_size, dropout):
         super().__init__()
         self.ffn = nn.Sequential(
-            nn.Conv1d(in_dim, hidden_dim, kernel_size=kernel_size,
-                      padding=(kernel_size - 1) // 2),
+            nn.Conv1d(
+                in_dim,
+                hidden_dim,
+                kernel_size=kernel_size,
+                padding=(kernel_size - 1) // 2,
+            ),
             nn.ReLU(),
-            nn.Conv1d(hidden_dim, in_dim, kernel_size=kernel_size,
-                      padding=(kernel_size - 1) // 2)
+            nn.Conv1d(
+                hidden_dim,
+                in_dim,
+                kernel_size=kernel_size,
+                padding=(kernel_size - 1) // 2,
+            ),
         )
         self.layer_norm = LayerNorm(in_dim)
         self.dropout = self.dropout_module = FairseqDropout(
@@ -57,8 +72,7 @@ class PositionwiseFeedForward(nn.Module):
 
 class FFTLayer(torch.nn.Module):
     def __init__(
-            self, embed_dim, n_heads, hidden_dim, kernel_size, dropout,
-            attention_dropout
+        self, embed_dim, n_heads, hidden_dim, kernel_size, dropout, attention_dropout
     ):
         super().__init__()
         self.self_attn = MultiheadAttention(
@@ -74,8 +88,7 @@ class FFTLayer(torch.nn.Module):
         residual = x
         x = x.transpose(0, 1)
         x, _ = self.self_attn(
-            query=x, key=x, value=x, key_padding_mask=padding_mask,
-            need_weights=False
+            query=x, key=x, value=x, key_padding_mask=padding_mask, need_weights=False
         )
         x = x.transpose(0, 1)
         x = self.layer_norm(x + residual)
@@ -106,11 +119,12 @@ class VariancePredictor(nn.Module):
         super().__init__()
         self.conv1 = nn.Sequential(
             nn.Conv1d(
-                args.encoder_embed_dim, args.var_pred_hidden_dim,
+                args.encoder_embed_dim,
+                args.var_pred_hidden_dim,
                 kernel_size=args.var_pred_kernel_size,
-                padding=(args.var_pred_kernel_size - 1) // 2
+                padding=(args.var_pred_kernel_size - 1) // 2,
             ),
-            nn.ReLU()
+            nn.ReLU(),
         )
         self.ln1 = nn.LayerNorm(args.var_pred_hidden_dim)
         self.dropout_module = FairseqDropout(
@@ -118,10 +132,12 @@ class VariancePredictor(nn.Module):
         )
         self.conv2 = nn.Sequential(
             nn.Conv1d(
-                args.var_pred_hidden_dim, args.var_pred_hidden_dim,
-                kernel_size=args.var_pred_kernel_size, padding=1
+                args.var_pred_hidden_dim,
+                args.var_pred_hidden_dim,
+                kernel_size=args.var_pred_kernel_size,
+                padding=1,
             ),
-            nn.ReLU()
+            nn.ReLU(),
         )
         self.ln2 = nn.LayerNorm(args.var_pred_hidden_dim)
         self.proj = nn.Linear(args.var_pred_hidden_dim, 1)
@@ -171,8 +187,15 @@ class VarianceAdaptor(nn.Module):
         return out, emb
 
     def forward(
-            self, x, padding_mask, durations=None, pitches=None, energies=None,
-            d_factor=1.0, p_factor=1.0, e_factor=1.0
+        self,
+        x,
+        padding_mask,
+        durations=None,
+        pitches=None,
+        energies=None,
+        d_factor=1.0,
+        p_factor=1.0,
+        e_factor=1.0,
     ):
         # x: B x T x C
         log_dur_out = self.duration_predictor(x)
@@ -205,8 +228,7 @@ class FastSpeech2Encoder(FairseqEncoder):
         self.spk_emb_proj = None
         if embed_speaker is not None:
             self.spk_emb_proj = nn.Linear(
-                args.encoder_embed_dim + args.speaker_embed_dim,
-                args.encoder_embed_dim
+                args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim
             )
 
         self.dropout_module = FairseqDropout(
@@ -224,9 +246,12 @@ class FastSpeech2Encoder(FairseqEncoder):
 
         self.encoder_fft_layers = nn.ModuleList(
             FFTLayer(
-                args.encoder_embed_dim, args.encoder_attention_heads,
-                args.fft_hidden_dim, args.fft_kernel_size,
-                dropout=args.dropout, attention_dropout=args.attention_dropout
+                args.encoder_embed_dim,
+                args.encoder_attention_heads,
+                args.fft_hidden_dim,
+                args.fft_kernel_size,
+                dropout=args.dropout,
+                attention_dropout=args.attention_dropout,
             )
             for _ in range(args.encoder_layers)
         )
@@ -235,9 +260,12 @@ class FastSpeech2Encoder(FairseqEncoder):
 
         self.decoder_fft_layers = nn.ModuleList(
             FFTLayer(
-                args.decoder_embed_dim, args.decoder_attention_heads,
-                args.fft_hidden_dim, args.fft_kernel_size,
-                dropout=args.dropout, attention_dropout=args.attention_dropout
+                args.decoder_embed_dim,
+                args.decoder_attention_heads,
+                args.fft_hidden_dim,
+                args.fft_kernel_size,
+                dropout=args.dropout,
+                attention_dropout=args.attention_dropout,
             )
             for _ in range(args.decoder_layers)
         )
@@ -247,15 +275,25 @@ class FastSpeech2Encoder(FairseqEncoder):
         self.postnet = None
         if args.add_postnet:
             self.postnet = Postnet(
-                self.out_dim, args.postnet_conv_dim,
+                self.out_dim,
+                args.postnet_conv_dim,
                 args.postnet_conv_kernel_size,
-                args.postnet_layers, args.postnet_dropout
+                args.postnet_layers,
+                args.postnet_dropout,
             )
 
         self.apply(model_init)
 
-    def forward(self, src_tokens, src_lengths=None, speaker=None,
-                durations=None, pitches=None, energies=None, **kwargs):
+    def forward(
+        self,
+        src_tokens,
+        src_lengths=None,
+        speaker=None,
+        durations=None,
+        pitches=None,
+        energies=None,
+        **kwargs
+    ):
         x = self.embed_tokens(src_tokens)
 
         enc_padding_mask = src_tokens.eq(self.padding_idx)
@@ -270,8 +308,9 @@ class FastSpeech2Encoder(FairseqEncoder):
             emb = self.embed_speaker(speaker).expand(bsz, seq_len, -1)
             x = self.spk_emb_proj(torch.cat([x, emb], dim=2))
 
-        x, out_lens, log_dur_out, pitch_out, energy_out = \
-            self.var_adaptor(x, enc_padding_mask, durations, pitches, energies)
+        x, out_lens, log_dur_out, pitch_out, energy_out = self.var_adaptor(
+            x, enc_padding_mask, durations, pitches, energies
+        )
 
         dec_padding_mask = lengths_to_padding_mask(out_lens)
         x += self.dec_pos_emb_alpha * self.embed_positions(dec_padding_mask)
@@ -326,7 +365,7 @@ class FastSpeech2Model(FairseqEncoderModel):
 
         out_dim = args.output_frame_dim * args.n_frames_per_step
         self.ctc_proj = None
-        if getattr(args, "ctc_weight", 0.) > 0.:
+        if getattr(args, "ctc_weight", 0.0) > 0.0:
             self.ctc_proj = nn.Linear(out_dim, len(src_dict))
 
     @classmethod

fairseq/models/text_to_speech/hifigan.py

@@ -119,7 +119,7 @@ class Generator(torch.nn.Module):
 
         self.ups = nn.ModuleList()
         for i, (u, k) in enumerate(
-                zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"])
+            zip(cfg["upsample_rates"], cfg["upsample_kernel_sizes"])
         ):
             self.ups.append(
                 weight_norm(
@@ -137,7 +137,7 @@ class Generator(torch.nn.Module):
         for i in range(len(self.ups)):
             ch = cfg["upsample_initial_channel"] // (2 ** (i + 1))
             for k, d in zip(
-                    cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"]
+                cfg["resblock_kernel_sizes"], cfg["resblock_dilation_sizes"]
             ):
                 self.resblocks.append(ResBlock(ch, k, d))
 

fairseq/models/text_to_speech/tacotron2.py

@@ -9,9 +9,13 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
-                            FairseqIncrementalDecoder, register_model,
-                            register_model_architecture)
+from fairseq.models import (
+    FairseqEncoder,
+    FairseqEncoderDecoderModel,
+    FairseqIncrementalDecoder,
+    register_model,
+    register_model_architecture,
+)
 from fairseq.modules import LSTMCellWithZoneOut, LocationAttention
 
 
@@ -31,29 +35,36 @@ class Tacotron2Encoder(FairseqEncoder):
         self.spk_emb_proj = None
         if embed_speaker is not None:
             self.spk_emb_proj = nn.Linear(
-                args.encoder_embed_dim + args.speaker_embed_dim,
-                args.encoder_embed_dim
+                args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim
             )
 
-        self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim,
-                                         padding_idx=self.padding_idx)
+        self.embed_tokens = nn.Embedding(
+            len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx
+        )
 
-        assert(args.encoder_conv_kernel_size % 2 == 1)
+        assert args.encoder_conv_kernel_size % 2 == 1
         self.convolutions = nn.ModuleList(
             nn.Sequential(
-                nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim,
-                          kernel_size=args.encoder_conv_kernel_size,
-                          padding=((args.encoder_conv_kernel_size - 1) // 2)),
+                nn.Conv1d(
+                    args.encoder_embed_dim,
+                    args.encoder_embed_dim,
+                    kernel_size=args.encoder_conv_kernel_size,
+                    padding=((args.encoder_conv_kernel_size - 1) // 2),
+                ),
                 nn.BatchNorm1d(args.encoder_embed_dim),
                 nn.ReLU(),
-                nn.Dropout(args.encoder_dropout)
+                nn.Dropout(args.encoder_dropout),
             )
             for _ in range(args.encoder_conv_layers)
         )
 
-        self.lstm = nn.LSTM(args.encoder_embed_dim, args.encoder_embed_dim // 2,
-                            num_layers=args.encoder_lstm_layers,
-                            batch_first=True, bidirectional=True)
+        self.lstm = nn.LSTM(
+            args.encoder_embed_dim,
+            args.encoder_embed_dim // 2,
+            num_layers=args.encoder_lstm_layers,
+            batch_first=True,
+            bidirectional=True,
+        )
 
         self.apply(encoder_init)
 
@@ -78,7 +89,7 @@ class Tacotron2Encoder(FairseqEncoder):
 
         return {
             "encoder_out": [x],  # B x T x C
-            "encoder_padding_mask": encoder_padding_mask,   # B x T
+            "encoder_padding_mask": encoder_padding_mask,  # B x T
         }
 
 
@@ -86,8 +97,7 @@ class Prenet(nn.Module):
     def __init__(self, in_dim, n_layers, n_units, dropout):
         super().__init__()
         self.layers = nn.ModuleList(
-            nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units),
-                          nn.ReLU())
+            nn.Sequential(nn.Linear(in_dim if i == 0 else n_units, n_units), nn.ReLU())
             for i in range(n_layers)
         )
         self.dropout = dropout
@@ -102,20 +112,24 @@ class Postnet(nn.Module):
     def __init__(self, in_dim, n_channels, kernel_size, n_layers, dropout):
         super(Postnet, self).__init__()
         self.convolutions = nn.ModuleList()
-        assert(kernel_size % 2 == 1)
+        assert kernel_size % 2 == 1
         for i in range(n_layers):
-            cur_layers = [
-                nn.Conv1d(in_dim if i == 0 else n_channels,
-                          n_channels if i < n_layers - 1 else in_dim,
-                          kernel_size=kernel_size,
-                          padding=((kernel_size - 1) // 2)),
-                nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim)
-            ] + ([nn.Tanh()] if i < n_layers - 1 else []) + [nn.Dropout(dropout)]
+            cur_layers = (
+                [
+                    nn.Conv1d(
+                        in_dim if i == 0 else n_channels,
+                        n_channels if i < n_layers - 1 else in_dim,
+                        kernel_size=kernel_size,
+                        padding=((kernel_size - 1) // 2),
+                    ),
+                    nn.BatchNorm1d(n_channels if i < n_layers - 1 else in_dim),
+                ]
+                + ([nn.Tanh()] if i < n_layers - 1 else [])
+                + [nn.Dropout(dropout)]
+            )
             nn.init.xavier_uniform_(
                 cur_layers[0].weight,
-                torch.nn.init.calculate_gain(
-                    "tanh" if i < n_layers - 1 else "linear"
-                )
+                torch.nn.init.calculate_gain("tanh" if i < n_layers - 1 else "linear"),
             )
             self.convolutions.append(nn.Sequential(*cur_layers))
 
@@ -138,21 +152,25 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
         self.n_frames_per_step = args.n_frames_per_step
         self.out_dim = args.output_frame_dim * args.n_frames_per_step
 
-        self.prenet = Prenet(self.out_dim, args.prenet_layers, args.prenet_dim,
-                             args.prenet_dropout)
+        self.prenet = Prenet(
+            self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout
+        )
 
         # take prev_context, prev_frame, (speaker embedding) as input
         self.attention_lstm = LSTMCellWithZoneOut(
             args.zoneout,
             args.prenet_dim + args.encoder_embed_dim,
-            args.decoder_lstm_dim
+            args.decoder_lstm_dim,
         )
 
         # take attention_lstm output, attention_state, encoder_out as input
         self.attention = LocationAttention(
-            args.attention_dim, args.encoder_embed_dim, args.decoder_lstm_dim,
+            args.attention_dim,
+            args.encoder_embed_dim,
+            args.decoder_lstm_dim,
             (1 + int(args.attention_use_cumprob)),
-            args.attention_conv_dim, args.attention_conv_kernel_size
+            args.attention_conv_dim,
+            args.attention_conv_kernel_size,
         )
 
         # take attention_lstm output, context, (gated_latent) as input
@@ -160,7 +178,7 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
             LSTMCellWithZoneOut(
                 args.zoneout,
                 args.encoder_embed_dim + args.decoder_lstm_dim,
-                args.decoder_lstm_dim
+                args.decoder_lstm_dim,
             )
             for i in range(args.decoder_lstm_layers)
         )
@@ -169,12 +187,16 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
         self.feat_proj = nn.Linear(proj_in_dim, self.out_dim)
         self.eos_proj = nn.Linear(proj_in_dim, 1)
 
-        self.postnet = Postnet(self.out_dim, args.postnet_conv_dim,
-                               args.postnet_conv_kernel_size,
-                               args.postnet_layers, args.postnet_dropout)
+        self.postnet = Postnet(
+            self.out_dim,
+            args.postnet_conv_dim,
+            args.postnet_conv_kernel_size,
+            args.postnet_layers,
+            args.postnet_dropout,
+        )
 
         self.ctc_proj = None
-        if getattr(args, "ctc_weight", 0.) > 0.:
+        if getattr(args, "ctc_weight", 0.0) > 0.0:
             self.ctc_proj = nn.Linear(self.out_dim, len(src_dict))
 
         self.apply(decoder_init)
@@ -190,12 +212,16 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
 
         lstm_h = self.get_incremental_state(incremental_state, "lstm_h")
         if lstm_h is None:
-            lstm_h = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-                      for _ in range(self.args.decoder_lstm_layers)]
+            lstm_h = [
+                enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
+                for _ in range(self.args.decoder_lstm_layers)
+            ]
         lstm_c = self.get_incremental_state(incremental_state, "lstm_c")
         if lstm_c is None:
-            lstm_c = [enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
-                      for _ in range(self.args.decoder_lstm_layers)]
+            lstm_c = [
+                enc_out.new_zeros(bsz, self.args.decoder_lstm_dim)
+                for _ in range(self.args.decoder_lstm_layers)
+            ]
 
         attn_w = self.get_incremental_state(incremental_state, "attn_w")
         if attn_w is None:
@@ -216,8 +242,14 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
         else:
             raise ValueError(f"{self.args.init_attn_c} not supported")
 
-    def forward(self, prev_output_tokens, encoder_out=None,
-                incremental_state=None, target_lengths=None, **kwargs):
+    def forward(
+        self,
+        prev_output_tokens,
+        encoder_out=None,
+        incremental_state=None,
+        target_lengths=None,
+        **kwargs,
+    ):
         enc_mask = encoder_out["encoder_padding_mask"]
         enc_out = encoder_out["encoder_out"][0]
         in_len = enc_out.size(1)
@@ -227,8 +259,9 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
         bsz, out_len, _ = prev_output_tokens.size()
 
         prenet_out = self.prenet(prev_output_tokens)
-        (alstm_h, alstm_c, lstm_h, lstm_c,
-         attn_w, attn_w_cum) = self._get_states(incremental_state, enc_out)
+        (alstm_h, alstm_c, lstm_h, lstm_c, attn_w, attn_w_cum) = self._get_states(
+            incremental_state, enc_out
+        )
         attn_ctx = self._get_init_attn_c(enc_out, enc_mask)
 
         attn_out = enc_out.new_zeros(bsz, in_len, out_len)
@@ -241,9 +274,7 @@ class Tacotron2Decoder(FairseqIncrementalDecoder):
             attn_state = attn_w.unsqueeze(1)
             if self.args.attention_use_cumprob:
                 attn_state = torch.stack((attn_w, attn_w_cum), dim=1)
-            attn_ctx, attn_w = self.attention(
-                enc_out, enc_mask, alstm_h, attn_state
-            )
+            attn_ctx, attn_w = self.attention(enc_out, enc_mask, alstm_h, attn_state)
             attn_w_cum = attn_w_cum + attn_w
             attn_out[:, :, t] = attn_w
 
@@ -297,7 +328,7 @@ class Tacotron2Model(FairseqEncoderDecoderModel):
         parser.add_argument("--postnet-conv-dim", type=int)
         parser.add_argument("--postnet-conv-kernel-size", type=int)
         parser.add_argument("--init-attn-c", type=str)
-        parser.add_argument("--attention-use-cumprob", action='store_true')
+        parser.add_argument("--attention-use-cumprob", action="store_true")
         parser.add_argument("--zoneout", type=float)
         parser.add_argument("--decoder-lstm-layers", type=int)
         parser.add_argument("--decoder-lstm-dim", type=int)
@@ -333,8 +364,7 @@ def base_architecture(args):
     # decoder
     args.attention_dim = getattr(args, "attention_dim", 128)
     args.attention_conv_dim = getattr(args, "attention_conv_dim", 32)
-    args.attention_conv_kernel_size = getattr(args,
-                                              "attention_conv_kernel_size", 15)
+    args.attention_conv_kernel_size = getattr(args, "attention_conv_kernel_size", 15)
     args.prenet_dropout = getattr(args, "prenet_dropout", 0.5)
     args.prenet_layers = getattr(args, "prenet_layers", 2)
     args.prenet_dim = getattr(args, "prenet_dim", 256)

fairseq/models/text_to_speech/tts_transformer.py

@@ -9,12 +9,14 @@ from typing import List, Optional
 import torch
 from torch import nn
 
-from fairseq.models import (FairseqEncoder, FairseqEncoderDecoderModel,
-                            FairseqIncrementalDecoder, register_model,
-                            register_model_architecture)
-from fairseq.modules import (
-    TransformerEncoderLayer, TransformerDecoderLayer
+from fairseq.models import (
+    FairseqEncoder,
+    FairseqEncoderDecoderModel,
+    FairseqIncrementalDecoder,
+    register_model,
+    register_model_architecture,
 )
+from fairseq.modules import TransformerEncoderLayer, TransformerDecoderLayer
 from fairseq.models.text_to_speech.tacotron2 import Prenet, Postnet
 from fairseq.modules import LayerNorm, PositionalEmbedding, FairseqDropout
 from fairseq.data.data_utils import lengths_to_padding_mask
@@ -42,30 +44,31 @@ class TTSTransformerEncoder(FairseqEncoder):
         self.spk_emb_proj = None
         if embed_speaker is not None:
             self.spk_emb_proj = nn.Linear(
-                args.encoder_embed_dim + args.speaker_embed_dim,
-                args.encoder_embed_dim
+                args.encoder_embed_dim + args.speaker_embed_dim, args.encoder_embed_dim
             )
 
         self.dropout_module = FairseqDropout(
             p=args.dropout, module_name=self.__class__.__name__
         )
-        self.embed_tokens = nn.Embedding(len(src_dict), args.encoder_embed_dim,
-                                         padding_idx=self.padding_idx)
-        assert(args.encoder_conv_kernel_size % 2 == 1)
+        self.embed_tokens = nn.Embedding(
+            len(src_dict), args.encoder_embed_dim, padding_idx=self.padding_idx
+        )
+        assert args.encoder_conv_kernel_size % 2 == 1
         self.prenet = nn.ModuleList(
             nn.Sequential(
-                nn.Conv1d(args.encoder_embed_dim, args.encoder_embed_dim,
-                          kernel_size=args.encoder_conv_kernel_size,
-                          padding=((args.encoder_conv_kernel_size - 1) // 2)),
+                nn.Conv1d(
+                    args.encoder_embed_dim,
+                    args.encoder_embed_dim,
+                    kernel_size=args.encoder_conv_kernel_size,
+                    padding=((args.encoder_conv_kernel_size - 1) // 2),
+                ),
                 nn.BatchNorm1d(args.encoder_embed_dim),
                 nn.ReLU(),
                 nn.Dropout(args.encoder_dropout),
             )
             for _ in range(args.encoder_conv_layers)
         )
-        self.prenet_proj = nn.Linear(
-            args.encoder_embed_dim, args.encoder_embed_dim
-        )
+        self.prenet_proj = nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim)
         self.embed_positions = PositionalEmbedding(
             args.max_source_positions, args.encoder_embed_dim, self.padding_idx
         )
@@ -112,7 +115,9 @@ class TTSTransformerEncoder(FairseqEncoder):
 
         return {
             "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [padding_mask] if padding_mask.any() else [],  # B x T
+            "encoder_padding_mask": [padding_mask]
+            if padding_mask.any()
+            else [],  # B x T
             "encoder_embedding": [],  # B x T x C
             "encoder_states": [],  # List[T x B x C]
             "src_tokens": [],
@@ -143,15 +148,15 @@ class TTSTransformerDecoder(FairseqIncrementalDecoder):
         )
         self.pos_emb_alpha = nn.Parameter(torch.ones(1))
         self.prenet = nn.Sequential(
-            Prenet(self.out_dim, args.prenet_layers, args.prenet_dim,
-                   args.prenet_dropout),
+            Prenet(
+                self.out_dim, args.prenet_layers, args.prenet_dim, args.prenet_dropout
+            ),
             nn.Linear(args.prenet_dim, args.decoder_embed_dim),
         )
 
         self.n_transformer_layers = args.decoder_transformer_layers
         self.transformer_layers = nn.ModuleList(
-            TransformerDecoderLayer(args)
-            for _ in range(self.n_transformer_layers)
+            TransformerDecoderLayer(args) for _ in range(self.n_transformer_layers)
         )
         if args.decoder_normalize_before:
             self.layer_norm = LayerNorm(args.decoder_embed_dim)
@@ -161,19 +166,28 @@ class TTSTransformerDecoder(FairseqIncrementalDecoder):
         self.feat_proj = nn.Linear(args.decoder_embed_dim, self.out_dim)
         self.eos_proj = nn.Linear(args.decoder_embed_dim, 1)
 
-        self.postnet = Postnet(self.out_dim, args.postnet_conv_dim,
-                               args.postnet_conv_kernel_size,
-                               args.postnet_layers, args.postnet_dropout)
+        self.postnet = Postnet(
+            self.out_dim,
+            args.postnet_conv_dim,
+            args.postnet_conv_kernel_size,
+            args.postnet_layers,
+            args.postnet_dropout,
+        )
 
         self.ctc_proj = None
-        if getattr(args, "ctc_weight", 0.) > 0.:
+        if getattr(args, "ctc_weight", 0.0) > 0.0:
             self.ctc_proj = nn.Linear(self.out_dim, len(src_dict))
 
         self.apply(decoder_init)
 
     def extract_features(
-            self, prev_outputs, encoder_out=None, incremental_state=None,
-            target_lengths=None, speaker=None, **kwargs
+        self,
+        prev_outputs,
+        encoder_out=None,
+        incremental_state=None,
+        target_lengths=None,
+        speaker=None,
+        **kwargs
     ):
         alignment_layer = self.n_transformer_layers - 1
         self_attn_padding_mask = lengths_to_padding_mask(target_lengths)
@@ -212,8 +226,8 @@ class TTSTransformerDecoder(FairseqIncrementalDecoder):
                 else None,
                 encoder_out["encoder_padding_mask"][0]
                 if (
-                        encoder_out is not None
-                        and len(encoder_out["encoder_padding_mask"]) > 0
+                    encoder_out is not None
+                    and len(encoder_out["encoder_padding_mask"]) > 0
                 )
                 else None,
                 incremental_state,
@@ -239,13 +253,22 @@ class TTSTransformerDecoder(FairseqIncrementalDecoder):
 
         return x, {"attn": attn, "inner_states": inner_states}
 
-    def forward(self, prev_output_tokens, encoder_out=None,
-                incremental_state=None, target_lengths=None, speaker=None,
-                **kwargs):
+    def forward(
+        self,
+        prev_output_tokens,
+        encoder_out=None,
+        incremental_state=None,
+        target_lengths=None,
+        speaker=None,
+        **kwargs
+    ):
         x, extra = self.extract_features(
-            prev_output_tokens, encoder_out=encoder_out,
-            incremental_state=incremental_state, target_lengths=target_lengths,
-            speaker=speaker, **kwargs
+            prev_output_tokens,
+            encoder_out=encoder_out,
+            incremental_state=incremental_state,
+            target_lengths=target_lengths,
+            speaker=speaker,
+            **kwargs
         )
         attn = extra["attn"]
         feat_out = self.feat_proj(x)
@@ -328,8 +351,9 @@ class TTSTransformerModel(FairseqEncoderDecoderModel):
         return cls(encoder, decoder)
 
     def forward_encoder(self, src_tokens, src_lengths, speaker=None, **kwargs):
-        return self.encoder(src_tokens, src_lengths=src_lengths,
-                            speaker=speaker, **kwargs)
+        return self.encoder(
+            src_tokens, src_lengths=src_lengths, speaker=speaker, **kwargs
+        )
 
     def set_num_updates(self, num_updates):
         super().set_num_updates(num_updates)
@@ -348,7 +372,9 @@ def base_architecture(args):
     # encoder transformer layers
     args.encoder_transformer_layers = getattr(args, "encoder_transformer_layers", 6)
     args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim)
+    args.encoder_ffn_embed_dim = getattr(
+        args, "encoder_ffn_embed_dim", 4 * args.encoder_embed_dim
+    )
     args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
     args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
     args.attention_dropout = getattr(args, "attention_dropout", 0.0)
@@ -366,6 +392,8 @@ def base_architecture(args):
     # decoder transformer layers
     args.decoder_transformer_layers = getattr(args, "decoder_transformer_layers", 6)
     args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
-    args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim)
+    args.decoder_ffn_embed_dim = getattr(
+        args, "decoder_ffn_embed_dim", 4 * args.decoder_embed_dim
+    )
     args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
     args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)

fairseq/models/text_to_speech/vocoder.py

@@ -13,7 +13,10 @@ from torch import nn
 import torch.nn.functional as F
 
 from fairseq.data.audio.audio_utils import (
-    get_window, get_fourier_basis, get_mel_filters, TTSSpectrogram
+    get_window,
+    get_fourier_basis,
+    get_mel_filters,
+    TTSSpectrogram,
 )
 from fairseq.data.audio.speech_to_text_dataset import S2TDataConfig
 from fairseq.models.text_to_speech.hifigan import Generator as HiFiGANModel
@@ -25,11 +28,9 @@ class PseudoInverseMelScale(torch.nn.Module):
     def __init__(self, n_stft, n_mels, sample_rate, f_min, f_max) -> None:
         super(PseudoInverseMelScale, self).__init__()
         self.n_mels = n_mels
-        basis = get_mel_filters(
-            sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max
-        )
+        basis = get_mel_filters(sample_rate, (n_stft - 1) * 2, n_mels, f_min, f_max)
         basis = torch.pinverse(basis)  # F x F_mel
-        self.register_buffer('basis', basis)
+        self.register_buffer("basis", basis)
 
     def forward(self, melspec: torch.Tensor) -> torch.Tensor:
         # pack batch
@@ -48,8 +49,12 @@ class PseudoInverseMelScale(torch.nn.Module):
 
 class GriffinLim(torch.nn.Module):
     def __init__(
-            self, n_fft: int, win_length: int, hop_length: int, n_iter: int,
-            window_fn=torch.hann_window
+        self,
+        n_fft: int,
+        win_length: int,
+        hop_length: int,
+        n_iter: int,
+        window_fn=torch.hann_window,
     ):
         super(GriffinLim, self).__init__()
         self.transform = TTSSpectrogram(
@@ -59,7 +64,7 @@ class GriffinLim(torch.nn.Module):
         basis = get_fourier_basis(n_fft)
         basis = torch.pinverse(n_fft / hop_length * basis).T[:, None, :]
         basis *= get_window(window_fn, n_fft, win_length)
-        self.register_buffer('basis', basis)
+        self.register_buffer("basis", basis)
 
         self.n_fft = n_fft
         self.win_length = win_length
@@ -70,33 +75,33 @@ class GriffinLim(torch.nn.Module):
 
     @classmethod
     def get_window_sum_square(
-            cls, n_frames, hop_length, win_length, n_fft,
-            window_fn=torch.hann_window
+        cls, n_frames, hop_length, win_length, n_fft, window_fn=torch.hann_window
     ) -> torch.Tensor:
         w_sq = get_window(window_fn, n_fft, win_length) ** 2
         n = n_fft + hop_length * (n_frames - 1)
         x = torch.zeros(n, dtype=torch.float32)
         for i in range(n_frames):
             ofst = i * hop_length
-            x[ofst: min(n, ofst + n_fft)] += w_sq[:max(0, min(n_fft, n - ofst))]
+            x[ofst : min(n, ofst + n_fft)] += w_sq[: max(0, min(n_fft, n - ofst))]
         return x
 
     def inverse(self, magnitude: torch.Tensor, phase) -> torch.Tensor:
         x = torch.cat(
-            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)],
-            dim=1
+            [magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
         )
         x = F.conv_transpose1d(x, self.basis, stride=self.hop_length)
         win_sum_sq = self.get_window_sum_square(
-            magnitude.shape[-1], hop_length=self.hop_length,
-            win_length=self.win_length, n_fft=self.n_fft
+            magnitude.shape[-1],
+            hop_length=self.hop_length,
+            win_length=self.win_length,
+            n_fft=self.n_fft,
         ).to(magnitude.device)
         # remove modulation effects
         approx_nonzero_indices = win_sum_sq > self.tiny
         x[:, :, approx_nonzero_indices] /= win_sum_sq[approx_nonzero_indices]
         x *= self.n_fft / self.hop_length
-        x = x[:, :, self.n_fft // 2:]
-        x = x[:, :, :-self.n_fft // 2:]
+        x = x[:, :, self.n_fft // 2 :]
+        x = x[:, :, : -self.n_fft // 2 :]
         return x
 
     def forward(self, specgram: torch.Tensor) -> torch.Tensor:
@@ -111,18 +116,33 @@ class GriffinLim(torch.nn.Module):
 
 
 class GriffinLimVocoder(nn.Module):
-    def __init__(self, sample_rate, win_size, hop_size, n_fft,
-                 n_mels, f_min, f_max, window_fn,
-                 spec_bwd_max_iter=32,
-                 fp16=False):
+    def __init__(
+        self,
+        sample_rate,
+        win_size,
+        hop_size,
+        n_fft,
+        n_mels,
+        f_min,
+        f_max,
+        window_fn,
+        spec_bwd_max_iter=32,
+        fp16=False,
+    ):
         super().__init__()
         self.inv_mel_transform = PseudoInverseMelScale(
-            n_stft=n_fft // 2 + 1, n_mels=n_mels, sample_rate=sample_rate,
-            f_min=f_min, f_max=f_max
+            n_stft=n_fft // 2 + 1,
+            n_mels=n_mels,
+            sample_rate=sample_rate,
+            f_min=f_min,
+            f_max=f_max,
         )
         self.gl_transform = GriffinLim(
-            n_fft=n_fft, win_length=win_size, hop_length=hop_size,
-            window_fn=window_fn, n_iter=spec_bwd_max_iter
+            n_fft=n_fft,
+            win_length=win_size,
+            hop_length=hop_size,
+            window_fn=window_fn,
+            n_iter=spec_bwd_max_iter,
         )
         if fp16:
             self.half()
@@ -151,17 +171,19 @@ class GriffinLimVocoder(nn.Module):
             sample_rate=feat_cfg["sample_rate"],
             win_size=int(feat_cfg["win_len_t"] * feat_cfg["sample_rate"]),
             hop_size=int(feat_cfg["hop_len_t"] * feat_cfg["sample_rate"]),
-            n_fft=feat_cfg["n_fft"], n_mels=feat_cfg["n_mels"],
-            f_min=feat_cfg["f_min"], f_max=feat_cfg["f_max"],
-            window_fn=window_fn, spec_bwd_max_iter=args.spec_bwd_max_iter,
-            fp16=args.fp16
+            n_fft=feat_cfg["n_fft"],
+            n_mels=feat_cfg["n_mels"],
+            f_min=feat_cfg["f_min"],
+            f_max=feat_cfg["f_max"],
+            window_fn=window_fn,
+            spec_bwd_max_iter=args.spec_bwd_max_iter,
+            fp16=args.fp16,
         )
 
 
 class HiFiGANVocoder(nn.Module):
     def __init__(
-            self, checkpoint_path: str, model_cfg: Dict[str, str],
-            fp16: bool = False
+        self, checkpoint_path: str, model_cfg: Dict[str, str], fp16: bool = False
     ) -> None:
         super().__init__()
         self.model = HiFiGANModel(model_cfg)

fairseq/models/transformer/transformer_decoder.py

@@ -29,8 +29,8 @@ from torch import Tensor
 
 # rewrite name for backward compatibility in `make_generation_fast_`
 def module_name_fordropout(module_name: str) -> str:
-    if module_name == 'TransformerDecoderBase':
-        return 'TransformerDecoder'
+    if module_name == "TransformerDecoderBase":
+        return "TransformerDecoder"
     else:
         return module_name
 

fairseq/models/transformer/transformer_encoder.py

@@ -29,8 +29,8 @@ from fairseq.models.transformer import (
 
 # rewrite name for backward compatibility in `make_generation_fast_`
 def module_name_fordropout(module_name: str) -> str:
-    if module_name == 'TransformerEncoderBase':
-        return 'TransformerEncoder'
+    if module_name == "TransformerEncoderBase":
+        return "TransformerEncoder"
     else:
         return module_name
 
@@ -232,7 +232,12 @@ class TransformerEncoderBase(FairseqEncoder):
         # `forward` so we use a dictionary instead.
         # TorchScript does not support mixed values so the values are all lists.
         # The empty list is equivalent to None.
-        src_lengths = src_tokens.ne(self.padding_idx).sum(dim=1, dtype=torch.int32).reshape(-1, 1).contiguous()
+        src_lengths = (
+            src_tokens.ne(self.padding_idx)
+            .sum(dim=1, dtype=torch.int32)
+            .reshape(-1, 1)
+            .contiguous()
+        )
         return {
             "encoder_out": [x],  # T x B x C
             "encoder_padding_mask": [encoder_padding_mask],  # B x T

fairseq/models/transformer_lm.py

@@ -15,7 +15,9 @@ from fairseq.models import (
     register_model_architecture,
 )
 from fairseq.models.transformer import (
-    DEFAULT_MIN_PARAMS_TO_WRAP, Embedding, TransformerDecoder
+    DEFAULT_MIN_PARAMS_TO_WRAP,
+    Embedding,
+    TransformerDecoder,
 )
 from fairseq.modules import AdaptiveInput, CharacterTokenEmbedder
 from fairseq.utils import safe_getattr, safe_hasattr
@@ -179,7 +181,7 @@ class TransformerLanguageModelConfig(FairseqDataclass):
                 "is set to 0 (i.e., always wrap) when --checkpoint-activations or "
                 "--offload-activations are passed."
             )
-        }
+        },
     )
     # config for "BASE Layers: Simplifying Training of Large, Sparse Models"
     base_layers: Optional[int] = field(
@@ -189,13 +191,25 @@ class TransformerLanguageModelConfig(FairseqDataclass):
         default=1, metadata={"help": "number of sublayers in each BASE layer"}
     )
     base_shuffle: Optional[int] = field(
-        default=1, metadata={"help": "shuffle tokens between workers before computing assignment"}
+        default=1,
+        metadata={"help": "shuffle tokens between workers before computing assignment"},
     )
     # NormFormer
-    scale_fc: Optional[bool] = field(default=False, metadata={"help": 'Insert LayerNorm between fully connected layers'})
-    scale_attn: Optional[bool] = field(default=False, metadata={"help": 'Insert LayerNorm after attention'})
-    scale_heads: Optional[bool] = field(default=False, metadata={"help": 'Learn a scale coefficient for each attention head'})
-    scale_resids: Optional[bool] = field(default=False, metadata={"help": 'Learn a scale coefficient for each residual connection'})
+    scale_fc: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Insert LayerNorm between fully connected layers"},
+    )
+    scale_attn: Optional[bool] = field(
+        default=False, metadata={"help": "Insert LayerNorm after attention"}
+    )
+    scale_heads: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Learn a scale coefficient for each attention head"},
+    )
+    scale_resids: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Learn a scale coefficient for each residual connection"},
+    )
     # options from other parts of the config
     add_bos_token: bool = II("task.add_bos_token")
     tokens_per_sample: int = II("task.tokens_per_sample")
@@ -345,7 +359,9 @@ def base_lm_architecture(args):
     args.decoder_output_dim = safe_getattr(
         args, "decoder_output_dim", args.decoder_embed_dim
     )
-    args.decoder_input_dim = safe_getattr(args, "decoder_input_dim", args.decoder_embed_dim)
+    args.decoder_input_dim = safe_getattr(
+        args, "decoder_input_dim", args.decoder_embed_dim
+    )
 
     # Model training is not stable without this
     args.decoder_normalize_before = True
@@ -362,10 +378,10 @@ def base_lm_architecture(args):
     args.layernorm_embedding = safe_getattr(args, "layernorm_embedding", False)
     args.checkpoint_activations = safe_getattr(args, "checkpoint_activations", False)
     args.offload_activations = safe_getattr(args, "offload_activations", False)
-    args.scale_fc = safe_getattr(args, 'scale_fc', False)
-    args.scale_attn = safe_getattr(args, 'scale_attn', False)
-    args.scale_heads = safe_getattr(args, 'scale_heads', False)
-    args.scale_resids = safe_getattr(args, 'scale_resids', False)
+    args.scale_fc = safe_getattr(args, "scale_fc", False)
+    args.scale_attn = safe_getattr(args, "scale_attn", False)
+    args.scale_heads = safe_getattr(args, "scale_heads", False)
+    args.scale_resids = safe_getattr(args, "scale_resids", False)
     if args.offload_activations:
         args.checkpoint_activations = True
 
@@ -387,7 +403,9 @@ def transformer_lm_baevski_wiki103(args):
     args.dropout = safe_getattr(args, "dropout", 0.3)
     args.adaptive_input = safe_getattr(args, "adaptive_input", True)
     args.tie_adaptive_weights = safe_getattr(args, "tie_adaptive_weights", True)
-    args.adaptive_input_cutoff = safe_getattr(args, "adaptive_input_cutoff", "20000,60000")
+    args.adaptive_input_cutoff = safe_getattr(
+        args, "adaptive_input_cutoff", "20000,60000"
+    )
     args.adaptive_softmax_cutoff = safe_getattr(
         args, "adaptive_softmax_cutoff", "20000,60000"
     )
@@ -472,7 +490,9 @@ def transformer_lm_gpt2_big(args):
 def base_gpt3_architecture(args):
     args.decoder_input_dim = args.decoder_embed_dim
     args.decoder_output_dim = args.decoder_embed_dim
-    args.decoder_ffn_embed_dim = safe_getattr(args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4)
+    args.decoder_ffn_embed_dim = safe_getattr(
+        args, "decoder_ffn_embed_dim", args.decoder_embed_dim * 4
+    )
     # GPT-3 used learned positional embeddings, rather than sinusoidal
     args.decoder_learned_pos = safe_getattr(args, "decoder_learned_pos", True)
     args.dropout = safe_getattr(args, "dropout", 0.0)

fairseq/models/wav2vec/wav2vec2.py

@@ -232,9 +232,11 @@ class Wav2Vec2Config(FairseqDataclass):
     )
 
     checkpoint_activations: bool = field(
-        default=False, metadata={"help": "recompute activations and save memory for extra compute"}
+        default=False,
+        metadata={"help": "recompute activations and save memory for extra compute"},
     )
 
+
 @register_model("wav2vec2", dataclass=Wav2Vec2Config)
 class Wav2Vec2Model(BaseFairseqModel):
     def __init__(self, cfg: Wav2Vec2Config):
@@ -844,14 +846,14 @@ class TransformerEncoder(nn.Module):
         layers = []
         for _ in range(args.encoder_layers):
             layer = TransformerSentenceEncoderLayer(
-                    embedding_dim=self.embedding_dim,
-                    ffn_embedding_dim=args.encoder_ffn_embed_dim,
-                    num_attention_heads=args.encoder_attention_heads,
-                    dropout=self.dropout,
-                    attention_dropout=args.attention_dropout,
-                    activation_dropout=args.activation_dropout,
-                    activation_fn=args.activation_fn,
-                    layer_norm_first=args.layer_norm_first,
+                embedding_dim=self.embedding_dim,
+                ffn_embedding_dim=args.encoder_ffn_embed_dim,
+                num_attention_heads=args.encoder_attention_heads,
+                dropout=self.dropout,
+                attention_dropout=args.attention_dropout,
+                activation_dropout=args.activation_dropout,
+                activation_fn=args.activation_fn,
+                layer_norm_first=args.layer_norm_first,
             )
             if args.checkpoint_activations:
                 layer = fsdp_wrap(layer)

fairseq/models/wav2vec/wav2vec2_asr.py

@@ -152,10 +152,12 @@ class Wav2Vec2AsrConfig(FairseqDataclass):
     w2v_args: Any = None
 
     checkpoint_activations: bool = field(
-        default=False, metadata={"help": "recompute activations and save memory for extra compute"}
+        default=False,
+        metadata={"help": "recompute activations and save memory for extra compute"},
     )
     ddp_backend: str = II("distributed_training.ddp_backend")
 
+
 @dataclass
 class Wav2Vec2CtcConfig(Wav2Vec2AsrConfig):
     blank_weight: float = 0
@@ -268,6 +270,7 @@ class Wav2Vec2Seq2SeqConfig(Wav2Vec2AsrConfig):
     )
     autoregressive: bool = II("task.autoregressive")
 
+
 @register_model("wav2vec_seq2seq", dataclass=Wav2Vec2Seq2SeqConfig)
 class Wav2Vec2Seq2SeqModel(FairseqEncoderDecoderModel):
     def __init__(self, encoder, decoder):
@@ -394,12 +397,17 @@ class Wav2VecEncoder(FairseqEncoder):
     def load_model_weights(self, state, model, cfg):
         if cfg.ddp_backend == "fully_sharded":
             from fairseq.distributed import FullyShardedDataParallel
+
             for name, module in model.named_modules():
                 if "encoder.layers" in name and len(name.split(".")) == 3:
                     # Only for layers, we do a special handling and load the weights one by one
                     # We dont load all weights together as that wont be memory efficient and may
                     # cause oom
-                    new_dict = {k.replace(name+".", "") : v for (k, v) in state["model"].items() if name+"." in k}
+                    new_dict = {
+                        k.replace(name + ".", ""): v
+                        for (k, v) in state["model"].items()
+                        if name + "." in k
+                    }
                     assert isinstance(module, FullyShardedDataParallel)
                     with module.summon_full_params():
                         module.load_state_dict(new_dict, strict=True)
@@ -409,7 +417,9 @@ class Wav2VecEncoder(FairseqEncoder):
             r = re.compile("encoder.layers.\d.")
             filtered_list = list(filter(r.match, state["model"].keys()))
 
-            new_big_dict = {k: v for (k, v) in state["model"].items() if k not in filtered_list}
+            new_big_dict = {
+                k: v for (k, v) in state["model"].items() if k not in filtered_list
+            }
 
             model.load_state_dict(new_big_dict, strict=False)
         else:
@@ -462,9 +472,9 @@ class Wav2VecEncoder(FairseqEncoder):
                 1, new_order
             )
         if encoder_out["padding_mask"] is not None:
-            encoder_out["padding_mask"] = encoder_out[
-                "padding_mask"
-            ].index_select(0, new_order)
+            encoder_out["padding_mask"] = encoder_out["padding_mask"].index_select(
+                0, new_order
+            )
         return encoder_out
 
     def max_positions(self):
@@ -640,7 +650,7 @@ class TransformerDecoder(FairseqIncrementalDecoder):
                     self_attn_mask=self.buffered_future_mask(x)
                     if incremental_state is None
                     else None,
-                    self_attn_padding_mask=self_attn_padding_mask
+                    self_attn_padding_mask=self_attn_padding_mask,
                 )
                 inner_states.append(x)
 

fairseq/modules/base_layer.py

@@ -12,14 +12,17 @@ from fairseq.modules.layer_norm import LayerNorm
 
 
 class BaseLayer(nn.Module):
-
     def __init__(self, args):
         super().__init__()
         self.num_workers = distributed_utils.get_data_parallel_world_size()
         expert_centroids = torch.empty(self.num_workers, args.decoder_embed_dim)
         torch.nn.init.orthogonal_(expert_centroids, gain=0.1)
-        self.register_parameter("expert_centroids", torch.nn.Parameter(expert_centroids))
-        self.expert_network = nn.Sequential(*([BaseSublayer(args) for _ in range(args.base_sublayers)]))
+        self.register_parameter(
+            "expert_centroids", torch.nn.Parameter(expert_centroids)
+        )
+        self.expert_network = nn.Sequential(
+            *([BaseSublayer(args) for _ in range(args.base_sublayers)])
+        )
         self.expert_id = distributed_utils.get_data_parallel_rank()
         self.shuffle = args.base_shuffle
         self.cpp = self.load_assignment()
@@ -39,20 +42,34 @@ class BaseLayer(nn.Module):
 
         with torch.no_grad():
             # Compute similarity of each token to each expert, for routing
-            token_expert_affinities = features.matmul(self.expert_centroids.transpose(0, 1))
+            token_expert_affinities = features.matmul(
+                self.expert_centroids.transpose(0, 1)
+            )
 
         # Compute which token goes to which expert
-        sort_by_expert, input_splits, output_splits = self.balanced_assignment(token_expert_affinities) \
-            if is_training else self.greedy_assignment(token_expert_affinities)
+        sort_by_expert, input_splits, output_splits = (
+            self.balanced_assignment(token_expert_affinities)
+            if is_training
+            else self.greedy_assignment(token_expert_affinities)
+        )
         # Swap these tokens for the right ones for our expert
-        routed_features = All2All.apply(features[sort_by_expert], output_splits, input_splits)
+        routed_features = All2All.apply(
+            features[sort_by_expert], output_splits, input_splits
+        )
 
         if routed_features.size(0) > 0:
             # Mix in the expert network based on how appropriate it is for these tokens
-            alpha = torch.sigmoid(routed_features.mv(self.expert_centroids[self.expert_id])).unsqueeze(1)
-            routed_features = alpha * self.expert_network(routed_features) + (1 - alpha) * routed_features
+            alpha = torch.sigmoid(
+                routed_features.mv(self.expert_centroids[self.expert_id])
+            ).unsqueeze(1)
+            routed_features = (
+                alpha * self.expert_network(routed_features)
+                + (1 - alpha) * routed_features
+            )
         # Return to original worker and ordering
-        result = All2All.apply(routed_features, input_splits, output_splits)[self.inverse_sort(sort_by_expert)]
+        result = All2All.apply(routed_features, input_splits, output_splits)[
+            self.inverse_sort(sort_by_expert)
+        ]
 
         if self.shuffle and is_training:
             # Undo shuffling
@@ -63,7 +80,9 @@ class BaseLayer(nn.Module):
 
     def inverse_sort(self, order):
         # Creates an index that undoes a sort: xs==xs[order][inverse_sort(order)]
-        return torch.empty_like(order).scatter_(0, order, torch.arange(0, order.size(0), device=order.device))
+        return torch.empty_like(order).scatter_(
+            0, order, torch.arange(0, order.size(0), device=order.device)
+        )
 
     def balanced_assignment(self, scores):
         ok = scores.isfinite()
@@ -79,7 +98,9 @@ class BaseLayer(nn.Module):
         worker2token = sort_ordering // k
 
         # Find how many tokens we're sending to each other worker (being careful for sending 0 tokens to some workers)
-        output_splits = torch.zeros((self.num_workers,), dtype=torch.long, device=scores.device)
+        output_splits = torch.zeros(
+            (self.num_workers,), dtype=torch.long, device=scores.device
+        )
         workers, counts = torch.unique_consecutive(token_to_workers, return_counts=True)
         output_splits[workers] = counts
         # Tell other workers how many tokens to expect from us
@@ -103,7 +124,7 @@ class BaseSublayer(nn.Module):
     def __init__(self, args):
         super().__init__()
         self.activation_fn = utils.get_activation_fn(
-            activation=getattr(args, 'activation_fn', 'relu') or "relu"
+            activation=getattr(args, "activation_fn", "relu") or "relu"
         )
         self.norm = LayerNorm(args.decoder_embed_dim, export=False)
         self.ff1 = torch.nn.Linear(args.decoder_embed_dim, args.decoder_ffn_embed_dim)
@@ -121,15 +142,29 @@ class All2All(torch.autograd.Function):
         ctx.input_splits = input_splits
         ctx.output_splits = output_splits
 
-        ys = torch.empty_like(xs) if output_splits is None else \
-            xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:]))
-        torch.distributed.all_to_all_single(ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits)
+        ys = (
+            torch.empty_like(xs)
+            if output_splits is None
+            else xs.new_empty(size=[sum(output_splits)] + list(xs.size()[1:]))
+        )
+        torch.distributed.all_to_all_single(
+            ys, xs, output_split_sizes=output_splits, input_split_sizes=input_splits
+        )
         return ys
 
     @staticmethod
     def backward(ctx, grad_output):
-        result = torch.empty_like(grad_output) if ctx.input_splits is None else \
-            grad_output.new_empty(size=[sum(ctx.input_splits)] + list(grad_output.size()[1:]))
-        torch.distributed.all_to_all_single(result, grad_output,
-                                            output_split_sizes=ctx.input_splits, input_split_sizes=ctx.output_splits)
+        result = (
+            torch.empty_like(grad_output)
+            if ctx.input_splits is None
+            else grad_output.new_empty(
+                size=[sum(ctx.input_splits)] + list(grad_output.size()[1:])
+            )
+        )
+        torch.distributed.all_to_all_single(
+            result,
+            grad_output,
+            output_split_sizes=ctx.input_splits,
+            input_split_sizes=ctx.output_splits,
+        )
         return result, None, None

fairseq/modules/checkpoint_activations.py

@@ -166,7 +166,9 @@ class CheckpointFunction(torch.autograd.Function):
         if parent_ctx_dict["offload"]:
             ctx.fwd_device = tuple(x.device for x in tensor_inputs)
             ctx.grad_requirements = tuple(x.requires_grad for x in tensor_inputs)
-            tensor_inputs = tuple(x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs)
+            tensor_inputs = tuple(
+                x.to(torch.device("cpu"), non_blocking=True) for x in tensor_inputs
+            )
 
         else:
             ctx.fwd_device, ctx.grad_requirements = None, None
@@ -199,7 +201,8 @@ class CheckpointFunction(torch.autograd.Function):
         tensor_inputs = checkpoint.detach_variable(tensor_inputs)
         if ctx.fwd_device is not None:
             tensor_inputs = [
-                t.to(ctx.fwd_device[i], non_blocking=True) for i, t in enumerate(tensor_inputs)
+                t.to(ctx.fwd_device[i], non_blocking=True)
+                for i, t in enumerate(tensor_inputs)
             ]
             for i, need_grad in enumerate(ctx.grad_requirements):
                 tensor_inputs[i].requires_grad = need_grad

fairseq/modules/gumbel_vector_quantizer.py

@@ -75,6 +75,7 @@ class GumbelVectorQuantizer(nn.Module):
 
         if isinstance(temp, str):
             import ast
+
             temp = ast.literal_eval(temp)
         assert len(temp) == 3, f"{temp}, {len(temp)}"
 

fairseq/modules/kmeans_attention.py

@@ -47,11 +47,12 @@ def cache_fn(f):
             return cache
         cache = f(*args, **kwargs)
         return cache
+
     return cached_fn
 
 
 def to(t):
-    return {'device': t.device, 'dtype': t.dtype}
+    return {"device": t.device, "dtype": t.dtype}
 
 
 def find_modules(nn_module, type):
@@ -102,7 +103,7 @@ def reshape_dim(t, dim, split_dims):
     shape = list(t.shape)
     num_dims = len(shape)
     dim = (dim + num_dims) % num_dims
-    shape[dim:dim+1] = split_dims
+    shape[dim : dim + 1] = split_dims
     return t.reshape(shape)
 
 
@@ -118,6 +119,7 @@ def ema_inplace(moving_avg, new, decay):
         return
     moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay))
 
+
 # helper classes
 
 
@@ -173,6 +175,7 @@ class ScaleNorm(nn.Module):
         def norm(t):
             n = torch.norm(t, dim=-1, keepdim=True).clamp(min=self.eps)
             return t / n * self.g
+
         return map_first_tuple_or_el(x, norm)
 
 
@@ -202,51 +205,62 @@ class MatrixMultiply(nn.Module):
             tensor = tensor.t()
         return x @ tensor
 
+
 # positional embeddings
 
 
 class DepthWiseConv1d(nn.Module):
     def __init__(self, dim_in, dim_out, kernel_size, stride=1, bias=True, causal=False):
         super().__init__()
-        self.padding = ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2)
+        self.padding = (
+            ((kernel_size - 1), 0) if causal else (kernel_size // 2, kernel_size // 2)
+        )
 
         self.net = nn.Sequential(
-            nn.Conv1d(dim_in, dim_in, kernel_size=kernel_size, groups=dim_in, stride=stride, bias=bias),
-            nn.Conv1d(dim_in, dim_out, 1, bias=bias)
+            nn.Conv1d(
+                dim_in,
+                dim_in,
+                kernel_size=kernel_size,
+                groups=dim_in,
+                stride=stride,
+                bias=bias,
+            ),
+            nn.Conv1d(dim_in, dim_out, 1, bias=bias),
         )
 
     def forward(self, x):
-        x = F.pad(x, self.padding, value=0.)
+        x = F.pad(x, self.padding, value=0.0)
         return self.net(x)
 
 
 class FixedPositionalEmbedding(nn.Module):
     def __init__(self, dim, max_seq_len):
         super().__init__()
-        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
         position = torch.arange(0, max_seq_len, dtype=torch.float)
         sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq)
         emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
-        self.register_buffer('emb', emb)
+        self.register_buffer("emb", emb)
 
     def forward(self, x):
-        return self.emb[None, :x.shape[1], :].to(x)
+        return self.emb[None, : x.shape[1], :].to(x)
 
 
 def rotate_every_two(x):
-    x = rearrange(x, '... (d j) -> ... d j', j=2)
+    x = rearrange(x, "... (d j) -> ... d j", j=2)
     x1, x2 = x.unbind(dim=-1)
     x = torch.stack((-x2, x1), dim=-1)
-    return rearrange(x, '... d j -> ... (d j)')
+    return rearrange(x, "... d j -> ... (d j)")
 
 
 def apply_rotary_pos_emb(q, k, sinu_pos):
-    sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j=2)
+    sinu_pos = rearrange(sinu_pos, "() n (j d) -> n j d", j=2)
     sin, cos = sinu_pos.unbind(dim=-2)
-    sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j=2), (sin, cos))
+    sin, cos = map(lambda t: repeat(t, "b n -> b (n j)", j=2), (sin, cos))
     q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
     return q, k
 
+
 # kmeans related function and class
 
 
@@ -261,7 +275,7 @@ def update_kmeans_on_backwards(module):
 
 
 def similarity(x, means):
-    return torch.einsum('bhld,hcd->bhlc', x, means)
+    return torch.einsum("bhld,hcd->bhlc", x, means)
 
 
 def dists_and_buckets(x, means):
@@ -303,13 +317,15 @@ def distribution(dists, window_size):
 
 
 class Kmeans(nn.Module):
-    def __init__(self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4):
+    def __init__(
+        self, num_heads, head_dim, num_clusters, ema_decay=0.999, commitment=1e-4
+    ):
         super().__init__()
         self.commitment = commitment
         self.ema_decay = ema_decay
 
-        self.register_buffer('means', torch.randn(num_heads, num_clusters, head_dim))
-        self.register_buffer('initted', torch.tensor(False))
+        self.register_buffer("means", torch.randn(num_heads, num_clusters, head_dim))
+        self.register_buffer("initted", torch.tensor(False))
         self.num_new_means = 0
         self.new_means = None
 
@@ -341,7 +357,7 @@ class Kmeans(nn.Module):
     @torch.no_grad()
     def update(self, new_means=None):
         new_means = default(new_means, self.new_means)
-        assert exists(new_means), 'new kmeans has not been supplied'
+        assert exists(new_means), "new kmeans has not been supplied"
         ema_inplace(self.means, new_means, self.ema_decay)
 
         del self.new_means
@@ -364,16 +380,33 @@ class Kmeans(nn.Module):
         if update_means:
             with torch.no_grad():
                 means = kmeans_iter(x, means, buckets)
-            self.new_means = ema(self.new_means, means, self.num_new_means / (self.num_new_means + 1))
+            self.new_means = ema(
+                self.new_means, means, self.num_new_means / (self.num_new_means + 1)
+            )
             self.num_new_means += 1
 
         return dists, loss
 
+
 # kmeans attention class
 
 
 class KmeansAttention(nn.Module):
-    def __init__(self, num_clusters, window_size, num_heads, head_dim, causal=False, dropout=0., ema_decay=0.999, commitment=1e-4, context_window_size=None, receives_context=False, num_mem_kv=0, shared_qk=False):
+    def __init__(
+        self,
+        num_clusters,
+        window_size,
+        num_heads,
+        head_dim,
+        causal=False,
+        dropout=0.0,
+        ema_decay=0.999,
+        commitment=1e-4,
+        context_window_size=None,
+        receives_context=False,
+        num_mem_kv=0,
+        shared_qk=False,
+    ):
         super().__init__()
         self.num_heads = num_heads
         self.num_clusters = num_clusters
@@ -389,18 +422,32 @@ class KmeansAttention(nn.Module):
         self.dropout = nn.Dropout(dropout)
 
         self.num_mem_kv = max(num_mem_kv, 1 if causal and not shared_qk else 0)
-        self.mem_key = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim))
-        self.mem_value = nn.Parameter(torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim))
+        self.mem_key = nn.Parameter(
+            torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
+        )
+        self.mem_value = nn.Parameter(
+            torch.randn(num_heads, num_clusters, self.num_mem_kv, head_dim)
+        )
 
     def forward(self, q, k, v, query_mask=None, key_mask=None, **kwargs):
-        b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = *q.shape, k.shape[2], self.window_size, self.context_window_size, self.num_clusters, q.device, q.dtype
-        is_reverse = kwargs.pop('_reverse', False)
+        b, h, t, d, kv_t, wsz, c_wsz, nc, device, dtype = (
+            *q.shape,
+            k.shape[2],
+            self.window_size,
+            self.context_window_size,
+            self.num_clusters,
+            q.device,
+            q.dtype,
+        )
+        is_reverse = kwargs.pop("_reverse", False)
 
         out = torch.zeros_like(q, dtype=dtype)
 
         update_kmeans = self.training and not is_reverse
 
-        key_mask = default(key_mask, query_mask) if not self.receives_context else key_mask
+        key_mask = (
+            default(key_mask, query_mask) if not self.receives_context else key_mask
+        )
         kv_wsz = wsz if not self.receives_context else c_wsz
 
         wsz = min(wsz, t)
@@ -424,16 +471,22 @@ class KmeansAttention(nn.Module):
         reshape_with_window = lambda x: x.reshape(b, h, nc, -1, d)
         q, k, v = map(reshape_with_window, (q, k, v))
 
-        m_k, m_v = map(lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value))
+        m_k, m_v = map(
+            lambda x: expand_dim(x, 0, b).to(q), (self.mem_key, self.mem_value)
+        )
         k, v = map(lambda x: torch.cat(x, dim=3), ((m_k, k), (m_v, v)))
 
-        dots = torch.einsum('bhnid,bhnjd->bhnij', q, k) * (d ** -0.5)
+        dots = torch.einsum("bhnid,bhnjd->bhnij", q, k) * (d ** -0.5)
 
         mask_value = max_neg_value(dots)
 
         if exists(query_mask) or exists(key_mask):
-            query_mask = default(query_mask, lambda: torch.ones((b, t), device=device).bool())
-            key_mask = default(key_mask, lambda: torch.ones((b, kv_t), device=device).bool())
+            query_mask = default(
+                query_mask, lambda: torch.ones((b, t), device=device).bool()
+            )
+            key_mask = default(
+                key_mask, lambda: torch.ones((b, kv_t), device=device).bool()
+            )
 
             q_mask = expand_dim(query_mask, 1, h).gather(2, indices)
             kv_mask = expand_dim(key_mask, 1, h).gather(2, kv_indices)
@@ -444,14 +497,18 @@ class KmeansAttention(nn.Module):
             del mask
 
         if self.causal:
-            q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices))
+            q_mask, kv_mask = map(
+                lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
+            )
             mask = q_mask[:, :, :, :, None] >= kv_mask[:, :, :, None, :]
             mask = F.pad(mask, (self.num_mem_kv, 0), value=1)
             dots.masked_fill_(~mask, mask_value)
             del mask
 
         if self.shared_qk:
-            q_mask, kv_mask = map(lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices))
+            q_mask, kv_mask = map(
+                lambda t: t.reshape(b, h, nc, -1), (indices, kv_indices)
+            )
             mask = q_mask[:, :, :, :, None] == kv_mask[:, :, :, None, :]
             mask = F.pad(mask, (self.num_mem_kv, 0), value=0)
             dots.masked_fill_(mask, TOKEN_SELF_ATTN_VALUE)
@@ -460,24 +517,32 @@ class KmeansAttention(nn.Module):
         dots = dots.softmax(dim=-1)
         dots = self.dropout(dots)
 
-        bo = torch.einsum('bhcij,bhcjd->bhcid', dots, v)
+        bo = torch.einsum("bhcij,bhcjd->bhcid", dots, v)
         so = torch.reshape(bo, (b, h, -1, bo.shape[-1])).type(dtype)
         out = scatter_mean(out, so, indices.unsqueeze(-1).expand_as(so), -2)
         return out, aux_loss
 
+
 # feedforward
 
 
 class GELU_(nn.Module):
     def forward(self, x):
-        return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        return (
+            0.5
+            * x
+            * (
+                1
+                + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))
+            )
+        )
 
 
-GELU = nn.GELU if hasattr(nn, 'GELU') else GELU_
+GELU = nn.GELU if hasattr(nn, "GELU") else GELU_
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, mult=4, dropout=0., activation=None, glu=False):
+    def __init__(self, dim, mult=4, dropout=0.0, activation=None, glu=False):
         super().__init__()
         activation = default(activation, GELU)
 
@@ -499,17 +564,49 @@ class FeedForward(nn.Module):
         x = self.w2(x)
         return x
 
+
 # self attention
 
 
 class SelfAttention(nn.Module):
-    def __init__(self, dim, max_seq_len, heads, local_attn_heads, window_size, dim_head=None, local_attn_window_size=None, local_attn_radius_blocks=1, causal=False, attn_dropout=0., dropout=0., kmeans_ema_decay=0.999, commitment_factor=1e-4, receives_context=False, context_window_size=None, rel_pos_emb=True, num_mem_kv=0, shared_qk=False, conv_query_kernel=9):
+    def __init__(
+        self,
+        dim,
+        max_seq_len,
+        heads,
+        local_attn_heads,
+        window_size,
+        dim_head=None,
+        local_attn_window_size=None,
+        local_attn_radius_blocks=1,
+        causal=False,
+        attn_dropout=0.0,
+        dropout=0.0,
+        kmeans_ema_decay=0.999,
+        commitment_factor=1e-4,
+        receives_context=False,
+        context_window_size=None,
+        rel_pos_emb=True,
+        num_mem_kv=0,
+        shared_qk=False,
+        conv_query_kernel=9,
+    ):
         super().__init__()
-        assert dim_head or (dim % heads) == 0, 'hidden dimension must be divisible by number of heads'
-        assert (max_seq_len % window_size) == 0, 'maximum sequence length must be divisible by the target window size'
-        assert local_attn_heads <= heads, 'number of local attention heads must be less than total heads'
-        assert not (receives_context and local_attn_heads > 0), 'local attention cannot be used for self attention with context'
-        assert not (receives_context and causal), 'contextual attention layer cannot be causal'
+        assert (
+            dim_head or (dim % heads) == 0
+        ), "hidden dimension must be divisible by number of heads"
+        assert (
+            max_seq_len % window_size
+        ) == 0, "maximum sequence length must be divisible by the target window size"
+        assert (
+            local_attn_heads <= heads
+        ), "number of local attention heads must be less than total heads"
+        assert not (
+            receives_context and local_attn_heads > 0
+        ), "local attention cannot be used for self attention with context"
+        assert not (
+            receives_context and causal
+        ), "contextual attention layer cannot be causal"
 
         local_attn_window_size = default(local_attn_window_size, window_size)
         context_window_size = default(context_window_size, window_size)
@@ -535,7 +632,15 @@ class SelfAttention(nn.Module):
 
         if self.local_attn_heads > 0:
             rel_pos_emb_config = (dim_head, local_attn_heads) if rel_pos_emb else None
-            self.local_attn = LocalAttention(dim=dim_head, window_size=local_attn_window_size, causal=causal, dropout=attn_dropout, rel_pos_emb_config=rel_pos_emb_config, look_backward=local_attn_radius_blocks, look_forward=0 if causal else local_attn_radius_blocks)
+            self.local_attn = LocalAttention(
+                dim=dim_head,
+                window_size=local_attn_window_size,
+                causal=causal,
+                dropout=attn_dropout,
+                rel_pos_emb_config=rel_pos_emb_config,
+                look_backward=local_attn_radius_blocks,
+                look_forward=0 if causal else local_attn_radius_blocks,
+            )
             self.local_to_qkv = nn.Linear(dim, 3 * local_dim_heads)
 
         # global
@@ -543,12 +648,24 @@ class SelfAttention(nn.Module):
         global_dim_heads = dim_head * self.global_attn_heads
 
         if self.global_attn_heads > 0:
-            self.global_attn = KmeansAttention(num_clusters, window_size, self.global_attn_heads, dim_head, causal=causal, dropout=attn_dropout, ema_decay=kmeans_ema_decay, commitment=commitment_factor, receives_context=receives_context, num_mem_kv=num_mem_kv, shared_qk=shared_qk)
+            self.global_attn = KmeansAttention(
+                num_clusters,
+                window_size,
+                self.global_attn_heads,
+                dim_head,
+                causal=causal,
+                dropout=attn_dropout,
+                ema_decay=kmeans_ema_decay,
+                commitment=commitment_factor,
+                receives_context=receives_context,
+                num_mem_kv=num_mem_kv,
+                shared_qk=shared_qk,
+            )
 
         self.to_q = nn.Sequential(
-            Rearrange('b n c -> b c n'),
+            Rearrange("b n c -> b c n"),
             DepthWiseConv1d(dim, global_dim_heads, conv_query_kernel, causal=causal),
-            Rearrange('b c n -> b n c')
+            Rearrange("b c n -> b n c"),
         )
 
         self.to_v = nn.Linear(dim, global_dim_heads, bias=False)
@@ -561,14 +678,30 @@ class SelfAttention(nn.Module):
         self.to_out = nn.Linear(dim_heads, dim, bias=False)
         self.dropout = nn.Dropout(dropout)
 
-    def forward(self, query, key, value, context=None, key_padding_mask=None, context_mask=None, pos_emb=None, **kwargs):
-        assert not (self.receives_context and not exists(context)), 'context must be passed if self attention is set to receive context'
+    def forward(
+        self,
+        query,
+        key,
+        value,
+        context=None,
+        key_padding_mask=None,
+        context_mask=None,
+        pos_emb=None,
+        **kwargs
+    ):
+        assert not (
+            self.receives_context and not exists(context)
+        ), "context must be passed if self attention is set to receive context"
         input_mask = key_padding_mask
         x = query.transpose(0, 1)
         b, t, _, h, dh = *x.shape, self.heads, self.dim_head
-        has_local, has_global = map(lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads))
+        has_local, has_global = map(
+            lambda x: x > 0, (self.local_attn_heads, self.global_attn_heads)
+        )
 
-        split_heads = lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous()
+        split_heads = (
+            lambda v: reshape_dim(v, -1, (-1, dh)).transpose(1, 2).contiguous()
+        )
 
         if has_local:
             local_qkv = self.local_to_qkv(x).chunk(3, dim=-1)
@@ -587,7 +720,7 @@ class SelfAttention(nn.Module):
             q, k, v = map(split_heads, (q, k, v))
 
         out = []
-        total_loss = torch.tensor(0., requires_grad=True, **to(x))
+        total_loss = torch.tensor(0.0, requires_grad=True, **to(x))
 
         if has_local:
             local_out = self.local_attn(lq, lk, lv, input_mask=input_mask)
@@ -597,7 +730,9 @@ class SelfAttention(nn.Module):
             if not self.receives_context and exists(pos_emb):
                 q, k = apply_rotary_pos_emb(q, k, pos_emb)
 
-            global_out, loss = self.global_attn(q, k, v, query_mask=input_mask, key_mask=context_mask)
+            global_out, loss = self.global_attn(
+                q, k, v, query_mask=input_mask, key_mask=context_mask
+            )
             total_loss = total_loss + loss
 
             out.append(global_out)

fairseq/modules/linearized_convolution.py

@@ -13,6 +13,7 @@ from .conv_tbc import ConvTBC
 from typing import Dict, Optional
 from torch import Tensor
 
+
 @with_incremental_state
 class LinearizedConvolution(ConvTBC):
     """An optimized version of nn.Conv1d.
@@ -41,7 +42,11 @@ class LinearizedConvolution(ConvTBC):
             del state_dict[prefix + "_linearized_weight"]
 
     @torch.jit.export
-    def forward(self, input, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None):
+    def forward(
+        self,
+        input,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
+    ):
         """
         Args:
             incremental_state: Used to buffer signal; if not None, then input is
@@ -80,18 +85,28 @@ class LinearizedConvolution(ConvTBC):
         return output.view(bsz, 1, -1)
 
     @torch.jit.unused
-    def reorder_incremental_state(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_order):
+    def reorder_incremental_state(
+        self,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
+        new_order,
+    ):
         input_buffer = self._get_input_buffer(incremental_state)
         if input_buffer is not None:
             input_buffer = input_buffer.index_select(0, new_order)
             self._set_input_buffer(incremental_state, input_buffer)
 
     @torch.jit.unused
-    def _get_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]):
+    def _get_input_buffer(
+        self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]]
+    ):
         return utils.get_incremental_state(self, incremental_state, "input_buffer")
 
     @torch.jit.unused
-    def _set_input_buffer(self, incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]], new_buffer):
+    def _set_input_buffer(
+        self,
+        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
+        new_buffer,
+    ):
         return utils.set_incremental_state(
             self, incremental_state, "input_buffer", new_buffer
         )

fairseq/modules/location_attention.py

@@ -20,9 +20,16 @@ class LocationAttention(nn.Module):
     :param int conv_kernel_size: filter size of attention convolution
     """
 
-    def __init__(self, attn_dim, encoder_dim, decoder_dim,
-                 attn_state_kernel_size, conv_dim, conv_kernel_size,
-                 scaling=2.0):
+    def __init__(
+        self,
+        attn_dim,
+        encoder_dim,
+        decoder_dim,
+        attn_state_kernel_size,
+        conv_dim,
+        conv_kernel_size,
+        scaling=2.0,
+    ):
         super(LocationAttention, self).__init__()
         self.attn_dim = attn_dim
         self.decoder_dim = decoder_dim
@@ -30,9 +37,13 @@ class LocationAttention(nn.Module):
         self.proj_enc = nn.Linear(encoder_dim, attn_dim)
         self.proj_dec = nn.Linear(decoder_dim, attn_dim, bias=False)
         self.proj_attn = nn.Linear(conv_dim, attn_dim, bias=False)
-        self.conv = nn.Conv1d(attn_state_kernel_size, conv_dim,
-                              2 * conv_kernel_size + 1,
-                              padding=conv_kernel_size, bias=False)
+        self.conv = nn.Conv1d(
+            attn_state_kernel_size,
+            conv_dim,
+            2 * conv_kernel_size + 1,
+            padding=conv_kernel_size,
+            bias=False,
+        )
         self.proj_out = nn.Sequential(nn.Tanh(), nn.Linear(attn_dim, 1))
 
         self.proj_enc_out = None  # cache

fairseq/modules/lstm_cell_with_zoneout.py

@@ -12,20 +12,20 @@ class LSTMCellWithZoneOut(nn.Module):
     https://arxiv.org/abs/1606.01305
     """
 
-    def __init__(self, prob: float, input_size: int, hidden_size: int,
-                 bias: bool = True):
+    def __init__(
+        self, prob: float, input_size: int, hidden_size: int, bias: bool = True
+    ):
         super(LSTMCellWithZoneOut, self).__init__()
         self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias)
         self.prob = prob
         if prob > 1.0 or prob < 0.0:
-            raise ValueError("zoneout probability must be in the range from "
-                             "0.0 to 1.0.")
+            raise ValueError(
+                "zoneout probability must be in the range from " "0.0 to 1.0."
+            )
 
     def zoneout(self, h, next_h, prob):
         if isinstance(h, tuple):
-            return tuple(
-                [self.zoneout(h[i], next_h[i], prob) for i in range(len(h))]
-            )
+            return tuple([self.zoneout(h[i], next_h[i], prob) for i in range(len(h))])
 
         if self.training:
             mask = h.new_zeros(*h.size()).bernoulli_(prob)

fairseq/modules/quantization/pq/utils.py

@@ -60,7 +60,9 @@ def quantize_model_(
           to layers_to_quantize[step]
     """
 
-    quantized_layers = get_layers(model, layers_to_quantize[step], remove_weights=remove_weights)
+    quantized_layers = get_layers(
+        model, layers_to_quantize[step], remove_weights=remove_weights
+    )
 
     for layer in quantized_layers:
 
@@ -108,8 +110,8 @@ def quantize_model_(
             centroids = torch.rand(centroids.size())
             centroids.cuda()
             # Get counts and assignment keys from layer in loaded checkpoint.
-            counts_key = layer+"."+"counts"
-            assignment_key = layer+"."+"assignments"
+            counts_key = layer + "." + "counts"
+            assignment_key = layer + "." + "assignments"
             # Get number of different bins to include.
             counts = list(state_dict[counts_key].shape)[0]
             print(layer)
@@ -122,7 +124,7 @@ def quantize_model_(
             print(num_assignments)
             print(num_extra)
             assignments_bins = torch.arange(counts)
-            assignments_rand = torch.randint(0, counts-1, (num_extra, ))
+            assignments_rand = torch.randint(0, counts - 1, (num_extra,))
             assignments = torch.cat((assignments_bins, assignments_rand), 0)
             # assignments = assignments.type(torch.IntTensor)
             assignments.cuda()

fairseq/modules/quantization/scalar/utils.py

@@ -16,7 +16,9 @@ from .modules import ActivationQuantizer, IntConv2d, IntEmbedding, IntLinear
 MAPPING = {nn.Linear: IntLinear, nn.Embedding: IntEmbedding, nn.Conv2d: IntConv2d}
 
 
-def quantize_model_(model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False):
+def quantize_model_(
+    model, p=0.2, bits=8, update_step=3000, method="histogram", remove_weights=False
+):
     """
     Replaces all modules with their scalar quantized counterpart and
     registers hooks to quantize the post-ativations of those modules.

fairseq/modules/transformer_layer.py

@@ -132,8 +132,7 @@ class TransformerEncoderLayerBase(nn.Module):
         # will become -inf, which results in NaN in model parameters
         if attn_mask is not None:
             attn_mask = attn_mask.masked_fill(
-                attn_mask.to(torch.bool),
-                -1e8 if x.dtype == torch.float32 else -1e4
+                attn_mask.to(torch.bool), -1e8 if x.dtype == torch.float32 else -1e4
             )
 
         residual = x
@@ -213,11 +212,19 @@ class TransformerDecoderLayerBase(nn.Module):
             add_bias_kv=add_bias_kv,
             add_zero_attn=add_zero_attn,
         )
-        self.attn_ln = LayerNorm(self.embed_dim) if utils.safe_getattr(cfg, 'scale_attn', False) else None
+        self.attn_ln = (
+            LayerNorm(self.embed_dim)
+            if utils.safe_getattr(cfg, "scale_attn", False)
+            else None
+        )
         self.nh = self.self_attn.num_heads
         self.head_dim = self.self_attn.head_dim
-        scale_heads = utils.safe_getattr(cfg, 'scale_heads', False)
-        self.c_attn = nn.Parameter(torch.ones((self.nh,)), requires_grad=True) if scale_heads else None
+        scale_heads = utils.safe_getattr(cfg, "scale_heads", False)
+        self.c_attn = (
+            nn.Parameter(torch.ones((self.nh,)), requires_grad=True)
+            if scale_heads
+            else None
+        )
 
         self.activation_fn = utils.get_activation_fn(activation=cfg.activation_fn)
         activation_dropout_p = cfg.activation_dropout
@@ -238,8 +245,21 @@ class TransformerDecoderLayerBase(nn.Module):
             self.encoder_attn = self.build_encoder_attention(self.embed_dim, cfg)
             self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=cfg.export)
 
-        self.ffn_layernorm = LayerNorm(cfg.decoder.ffn_embed_dim) if utils.safe_getattr(cfg, 'scale_fc', False) else None
-        self.w_resid = nn.Parameter(torch.ones(self.embed_dim, ), requires_grad=True) if utils.safe_getattr(cfg, 'scale_resids', False) else None
+        self.ffn_layernorm = (
+            LayerNorm(cfg.decoder.ffn_embed_dim)
+            if utils.safe_getattr(cfg, "scale_fc", False)
+            else None
+        )
+        self.w_resid = (
+            nn.Parameter(
+                torch.ones(
+                    self.embed_dim,
+                ),
+                requires_grad=True,
+            )
+            if utils.safe_getattr(cfg, "scale_resids", False)
+            else None
+        )
 
         self.fc1 = self.build_fc1(
             self.embed_dim,
@@ -297,7 +317,6 @@ class TransformerDecoderLayerBase(nn.Module):
     def residual_connection(self, x, residual):
         return residual + x
 
-
     def forward(
         self,
         x,
@@ -377,7 +396,7 @@ class TransformerDecoderLayerBase(nn.Module):
         if self.c_attn is not None:
             tgt_len, bsz = x.size(0), x.size(1)
             x = x.view(tgt_len, bsz, self.nh, self.head_dim)
-            x = torch.einsum('tbhd,h->tbhd', x, self.c_attn)
+            x = torch.einsum("tbhd,h->tbhd", x, self.c_attn)
             x = x.reshape(tgt_len, bsz, self.embed_dim)
         if self.attn_ln is not None:
             x = self.attn_ln(x)

fairseq/modules/transformer_sentence_encoder.py

@@ -35,9 +35,7 @@ def init_bert_params(module):
     def normal_(data):
         # with FSDP, module params will be on CUDA, so we cast them back to CPU
         # so that the RNG is consistent with and without FSDP
-        data.copy_(
-            data.cpu().normal_(mean=0.0, std=0.02).to(data.device)
-        )
+        data.copy_(data.cpu().normal_(mean=0.0, std=0.02).to(data.device))
 
     if isinstance(module, nn.Linear):
         normal_(module.weight.data)
@@ -276,7 +274,9 @@ class TransformerSentenceEncoder(nn.Module):
             inner_states.append(x)
 
         for layer in self.layers:
-            x, _ = layer(x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask)
+            x, _ = layer(
+                x, self_attn_padding_mask=padding_mask, self_attn_mask=attn_mask
+            )
             if not last_state_only:
                 inner_states.append(x)
 

fairseq/ngram_repeat_block.py

@@ -2,13 +2,13 @@
 # Licensed under the MIT License.
 
 """ Wrapper for ngram_repeat_block cuda extension """
+import math
+import warnings
+from typing import Dict, List, Optional
+
 import torch
 from torch import nn
 
-import math
-from typing import Dict, List, Optional
-import warnings
-
 try:
     from fairseq import ngram_repeat_block_cuda
 
@@ -37,7 +37,7 @@ def is_cuda_extension_usable() -> bool:
 
 
 class NGramRepeatBlock(nn.Module):
-    """ Wrapper class for calling ngram_repeat_block cuda extension """
+    """Wrapper class for calling ngram_repeat_block cuda extension"""
 
     def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True):
         super().__init__()

fairseq/optim/adam.py

@@ -67,13 +67,13 @@ class FairseqAdam(FairseqOptimizer):
         elif use_fused_adam:
             logger.info("using FusedAdam")
             self._optimizer = fused_adam_cls(
-                params,
-                use_fp16_stats=self.cfg.fp16_adam_stats,
-                **self.optimizer_config
+                params, use_fp16_stats=self.cfg.fp16_adam_stats, **self.optimizer_config
             )
         else:
             if self.cfg.fp16_adam_stats:
-                raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
+                raise NotImplementedError(
+                    "--fp16-adam-stats is only supported with FusedAdamV1"
+                )
             self._optimizer = Adam(params, **self.optimizer_config)
 
     @property

fairseq/optim/amp_optimizer.py

@@ -63,8 +63,9 @@ class AMPOptimizer(optim.FairseqOptimizer):
                     ).format(self.min_loss_scale, new_loss_scale)
                 )
             else:
-                logger.info("AMP: overflow detected, setting scale to "
-                            f"to {new_loss_scale}")
+                logger.info(
+                    "AMP: overflow detected, setting scale to " f"to {new_loss_scale}"
+                )
         return grad_norm
 
     @property

fairseq/optim/composite.py

@@ -23,7 +23,9 @@ class OptimizerAndSchedulerConfig(FairseqDataclass):
     optimizer: Any = None
     lr_scheduler: Optional[Any] = None
     lr: List = II("optimization.lr")
-    lr_float: Optional[float] = None  # this makes it easier to sweep on learning rate with auto sweepers
+    lr_float: Optional[
+        float
+    ] = None  # this makes it easier to sweep on learning rate with auto sweepers
 
 
 @dataclass

fairseq/optim/cpu_adam.py

@@ -16,6 +16,7 @@ from omegaconf import II, DictConfig
 
 try:
     import deepspeed
+
     has_deepspeed = True
 except ImportError as e:
     has_deepspeed = False
@@ -24,12 +25,15 @@ except ImportError as e:
 def _get_cpu_adam():
     try:
         from deepspeed.ops.op_builder import CPUAdamBuilder
+
         return CPUAdamBuilder().load()
     except ImportError:
         # fbcode
         from deepspeed.ops.adam import DeepSpeedCPUAdam as ds_opt_adam
+
         return ds_opt_adam
 
+
 @dataclass
 class FairseqCPUAdamConfig(FairseqDataclass):
     adam_betas: str = field(

fairseq/optim/fp16_optimizer.py

@@ -64,9 +64,9 @@ class _FP16OptimizerMixin(object):
             fp32_params = []
             for p in params:
                 p32 = torch.nn.Parameter(p.data.float())
-                if hasattr(p, 'expert'):
+                if hasattr(p, "expert"):
                     p32.expert = True
-                elif hasattr(p, 'base_expert'):
+                elif hasattr(p, "base_expert"):
                     p32.base_expert = True
                 p32.grad = torch.zeros_like(p32.data)
                 if hasattr(p, "param_group"):
@@ -209,7 +209,9 @@ class _FP16OptimizerMixin(object):
         self._sync_fp16_grads_to_fp32()
 
         if getattr(self, "supports_step_with_scale", False):
-            self.fp32_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
+            self.fp32_optimizer.step(
+                closure, scale=(1.0 / self._multiply_factor), groups=groups
+            )
         else:
             self._unscale_grads()
             self.fp32_optimizer.step(closure, groups=groups)
@@ -434,7 +436,9 @@ class _MemoryEfficientFP16OptimizerMixin(object):
         """Performs a single optimization step."""
         if getattr(self, "supports_step_with_scale", False):
             # NOTE(msb) optimizer divides by scale factor
-            self.wrapped_optimizer.step(closure, scale=(1.0 / self._multiply_factor), groups=groups)
+            self.wrapped_optimizer.step(
+                closure, scale=(1.0 / self._multiply_factor), groups=groups
+            )
         else:
             self._unscale_grads()
             self.wrapped_optimizer.step(closure, groups=groups)

fairseq/optim/fused_adam.py

@@ -179,7 +179,7 @@ class FusedAdamV1(torch.optim.Optimizer):
 
                 if p.device.type == "cpu":
                     p_data_fp32 = p.data.cuda(non_blocking=True).float()
-                    out_p = torch.tensor([], dtype = torch.float)
+                    out_p = torch.tensor([], dtype=torch.float)
                 else:
                     p_data_fp32 = p.data.float()
                     out_p = p.data
@@ -234,6 +234,7 @@ class FusedAdamV1(torch.optim.Optimizer):
                     p.data.copy_(p_data_fp32, non_blocking=True)
 
                 if self.use_fp16_stats:
+
                     def inf_norm(t):
                         return torch.norm(t, float("inf"))
 
@@ -262,7 +263,9 @@ try:
 
         def __init__(self, *args, use_fp16_stats=False, **kwargs):
             if use_fp16_stats:
-                raise NotImplementedError("--fp16-adam-stats is only supported with FusedAdamV1")
+                raise NotImplementedError(
+                    "--fp16-adam-stats is only supported with FusedAdamV1"
+                )
             super().__init__(*args, **kwargs)
             if not hasattr(self, "multi_tensor_adam"):
                 raise Exception(

fairseq/optim/lr_scheduler/manual_lr_scheduler.py

@@ -32,7 +32,7 @@ class ManualSchedule(LegacyFairseqLRScheduler):
         self.optimizer.set_lr(self.lr)  # Set the beginning of the epoch.
 
     def parse_manuallr_args(self, lr_args_str):
-        lr_dict = ast.literal_eval(lr_args_str.replace(' ', ''))
+        lr_dict = ast.literal_eval(lr_args_str.replace(" ", ""))
         if not isinstance(lr_dict, dict):
             raise ValueError("epoch2lr/update2lr must be abel to evaluated to a dict")
 
@@ -84,9 +84,14 @@ class ManualSchedule(LegacyFairseqLRScheduler):
         if manual_keys:
             manual_lr = self.epoch2lr[max(manual_keys)]
         else:
-            logger.warning("@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format(
-                epoch, list(self.epoch2lr.items())[:min(10, len(self.epoch2lr.keys())-1)]
-            ))
+            logger.warning(
+                "@@@ epoch={} does not exist in manual lr input. epoch2lr={}...".format(
+                    epoch,
+                    list(self.epoch2lr.items())[
+                        : min(10, len(self.epoch2lr.keys()) - 1)
+                    ],
+                )
+            )
             manual_lr = self.optimizer.get_lr()
         return manual_lr
 
@@ -102,8 +107,14 @@ class ManualSchedule(LegacyFairseqLRScheduler):
         if manual_keys:
             manual_lr = self.update2lr[max(manual_keys)]
         else:
-            logger.warning("epoch={} does not exist in manual lr input update2lr={}...".format(
-                num_updates, list(self.update2lr.items())[:min(10, len(self.update2lr.keys())-1)]))
+            logger.warning(
+                "epoch={} does not exist in manual lr input update2lr={}...".format(
+                    num_updates,
+                    list(self.update2lr.items())[
+                        : min(10, len(self.update2lr.keys()) - 1)
+                    ],
+                )
+            )
             manual_lr = self.optimizer.get_lr()
 
         self.optimizer.set_lr(manual_lr)

fairseq/optim/lr_scheduler/step_lr_scheduler.py

@@ -36,8 +36,7 @@ class StepLRScheduleConfig(FairseqDataclass):
 
 @register_lr_scheduler("step", dataclass=StepLRScheduleConfig)
 class StepLRSchedule(FairseqLRScheduler):
-    """Decay learning rate every k updates by a fixed factor
-    """
+    """Decay learning rate every k updates by a fixed factor"""
 
     def __init__(self, cfg: StepLRScheduleConfig, fairseq_optimizer):
         super().__init__(cfg, fairseq_optimizer)
@@ -50,16 +49,16 @@ class StepLRSchedule(FairseqLRScheduler):
             cfg.warmup_init_lr if cfg.warmup_init_lr >= 0 else self.min_lr
         )
 
-        assert(self.lr_deacy_period > 0)
-        assert(self.lr_decay <= 1)
-        assert(self.min_lr >= 0)
-        assert(self.max_lr > self.min_lr)
+        assert self.lr_deacy_period > 0
+        assert self.lr_decay <= 1
+        assert self.min_lr >= 0
+        assert self.max_lr > self.min_lr
 
         if cfg.warmup_updates > 0:
             # linearly warmup for the first cfg.warmup_updates
             self.warmup_lr_step = (
-                (self.max_lr - self.warmup_init_lr) / self.warmup_updates
-            )
+                self.max_lr - self.warmup_init_lr
+            ) / self.warmup_updates
         else:
             self.warmup_lr_step = 1
 

fairseq/sequence_generator.py

@@ -171,7 +171,9 @@ class SequenceGenerator(nn.Module):
                 yield id, src, ref, hypos[i]
 
     @torch.no_grad()
-    def generate(self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs) -> List[List[Dict[str, Tensor]]]:
+    def generate(
+        self, models, sample: Dict[str, Dict[str, Tensor]], **kwargs
+    ) -> List[List[Dict[str, Tensor]]]:
         """Generate translations. Match the api of other fairseq generators.
 
         Args:
@@ -223,7 +225,10 @@ class SequenceGenerator(nn.Module):
                 else torch.tensor(src_tokens.size(-1)).to(src_tokens)
             )
         else:
-            raise Exception("expected src_tokens or source in net input. input keys: " + str(net_input.keys()))
+            raise Exception(
+                "expected src_tokens or source in net input. input keys: "
+                + str(net_input.keys())
+            )
 
         # bsz: total number of sentences in beam
         # Note that src_tokens may have more than 2 dimensions (i.e. audio features)
@@ -328,7 +333,9 @@ class SequenceGenerator(nn.Module):
                 encoder_outs = self.model.reorder_encoder_out(
                     encoder_outs, reorder_state
                 )
-            with torch.autograd.profiler.record_function("EnsembleModel: forward_decoder"):
+            with torch.autograd.profiler.record_function(
+                "EnsembleModel: forward_decoder"
+            ):
                 lprobs, avg_attn_scores = self.model.forward_decoder(
                     tokens[:, : step + 1],
                     encoder_outs,
@@ -751,7 +758,14 @@ class EnsembleModel(nn.Module):
         return self.has_incremental
 
     def max_decoder_positions(self):
-        return min([m.max_decoder_positions() for m in self.models if hasattr(m, "max_decoder_positions")] + [sys.maxsize])
+        return min(
+            [
+                m.max_decoder_positions()
+                for m in self.models
+                if hasattr(m, "max_decoder_positions")
+            ]
+            + [sys.maxsize]
+        )
 
     @torch.jit.export
     def forward_encoder(self, net_input: Dict[str, Tensor]):

fairseq/speech_generator.py

@@ -35,8 +35,12 @@ class SpeechGenerator(object):
 
 class AutoRegressiveSpeechGenerator(SpeechGenerator):
     def __init__(
-            self, model, vocoder, data_cfg, max_iter: int = 6000,
-            eos_prob_threshold: float = 0.5,
+        self,
+        model,
+        vocoder,
+        data_cfg,
+        max_iter: int = 6000,
+        eos_prob_threshold: float = 0.5,
     ):
         super().__init__(model, vocoder, data_cfg)
         self.max_iter = max_iter
@@ -54,8 +58,9 @@ class AutoRegressiveSpeechGenerator(SpeechGenerator):
         raw_dim = out_dim // n_frames_per_step
 
         # initialize
-        encoder_out = model.forward_encoder(src_tokens, src_lengths,
-                                            speaker=sample["speaker"])
+        encoder_out = model.forward_encoder(
+            src_tokens, src_lengths, speaker=sample["speaker"]
+        )
         incremental_state = {}
         feat, attn, eos_prob = [], [], []
         finished = src_tokens.new_zeros((bsz,)).bool()
@@ -66,21 +71,24 @@ class AutoRegressiveSpeechGenerator(SpeechGenerator):
             cur_out_lens = out_lens.clone()
             cur_out_lens.masked_fill_(cur_out_lens.eq(self.max_iter), step + 1)
             _, cur_eos_out, cur_extra = model.forward_decoder(
-                prev_feat_out, encoder_out=encoder_out,
+                prev_feat_out,
+                encoder_out=encoder_out,
                 incremental_state=incremental_state,
-                target_lengths=cur_out_lens, speaker=sample["speaker"], **kwargs
+                target_lengths=cur_out_lens,
+                speaker=sample["speaker"],
+                **kwargs
             )
             cur_eos_prob = torch.sigmoid(cur_eos_out).squeeze(2)
-            feat.append(cur_extra['feature_out'])
-            attn.append(cur_extra['attn'])
+            feat.append(cur_extra["feature_out"])
+            attn.append(cur_extra["attn"])
             eos_prob.append(cur_eos_prob)
 
-            cur_finished = (cur_eos_prob.squeeze(1) > self.eos_prob_threshold)
+            cur_finished = cur_eos_prob.squeeze(1) > self.eos_prob_threshold
             out_lens.masked_fill_((~finished) & cur_finished, step + 1)
             finished = finished | cur_finished
             if finished.sum().item() == bsz:
                 break
-            prev_feat_out = cur_extra['feature_out']
+            prev_feat_out = cur_extra["feature_out"]
 
         feat = torch.cat(feat, dim=1)
         feat = model.decoder.postnet(feat) + feat
@@ -98,11 +106,11 @@ class AutoRegressiveSpeechGenerator(SpeechGenerator):
 
         finalized = [
             {
-                'feature': feat[b, :out_len],
-                'eos_prob': eos_prob[b, :out_len],
-                'attn': attn[b, :, :out_len],
-                'alignment': alignment[b, :out_len],
-                'waveform': self.get_waveform(feat[b, :out_len]),
+                "feature": feat[b, :out_len],
+                "eos_prob": eos_prob[b, :out_len],
+                "attn": attn[b, :, :out_len],
+                "alignment": alignment[b, :out_len],
+                "waveform": self.get_waveform(feat[b, :out_len]),
             }
             for b, out_len in zip(range(bsz), out_lens)
         ]
@@ -134,7 +142,7 @@ class NonAutoregressiveSpeechGenerator(SpeechGenerator):
             prev_output_tokens=sample["net_input"]["prev_output_tokens"],
             incremental_state=None,
             target_lengths=sample["target_lengths"],
-            speaker=sample["speaker"]
+            speaker=sample["speaker"],
         )
         if feat_post is not None:
             feat = feat_post
@@ -142,9 +150,7 @@ class NonAutoregressiveSpeechGenerator(SpeechGenerator):
         feat = feat.view(bsz, -1, raw_dim)
         feat = self.gcmvn_denormalize(feat)
 
-        dur_out = torch.clamp(
-            torch.round(torch.exp(log_dur_out) - 1).long(), min=0
-        )
+        dur_out = torch.clamp(torch.round(torch.exp(log_dur_out) - 1).long(), min=0)
 
         def get_dur_plot_data(d):
             r = []
@@ -155,11 +161,11 @@ class NonAutoregressiveSpeechGenerator(SpeechGenerator):
         out_lens = out_lens * n_frames_per_step
         finalized = [
             {
-                'feature': feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]),
-                'waveform': self.get_waveform(
+                "feature": feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim]),
+                "waveform": self.get_waveform(
                     feat[b, :l] if l > 0 else feat.new_zeros([1, raw_dim])
                 ),
-                'attn': feat.new_tensor(get_dur_plot_data(dur_out[b])),
+                "attn": feat.new_tensor(get_dur_plot_data(dur_out[b])),
             }
             for b, l in zip(range(bsz), out_lens)
         ]
@@ -188,8 +194,12 @@ class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator)
         bsz = src_tokens.shape[0]
 
         feat, eos_prob, extra = model(
-            src_tokens, src_lens, prev_out_tokens, incremental_state=None,
-            target_lengths=tgt_lens, speaker=sample["speaker"]
+            src_tokens,
+            src_lens,
+            prev_out_tokens,
+            incremental_state=None,
+            target_lengths=tgt_lens,
+            speaker=sample["speaker"],
         )
 
         attn = extra["attn"]  # B x T_s x T_t
@@ -203,11 +213,11 @@ class TeacherForcingAutoRegressiveSpeechGenerator(AutoRegressiveSpeechGenerator)
 
         finalized = [
             {
-                'feature': feat[b, :tgt_len],
-                'eos_prob': eos_prob[b, :tgt_len],
-                'attn': attn[b, :, :tgt_len],
-                'alignment': alignment[b, :tgt_len],
-                'waveform': self.get_waveform(feat[b, :tgt_len]),
+                "feature": feat[b, :tgt_len],
+                "eos_prob": eos_prob[b, :tgt_len],
+                "attn": attn[b, :, :tgt_len],
+                "alignment": alignment[b, :tgt_len],
+                "waveform": self.get_waveform(feat[b, :tgt_len]),
             }
             for b, tgt_len in zip(range(bsz), tgt_lens)
         ]

fairseq/tasks/audio_finetuning.py

@@ -67,31 +67,31 @@ class AudioFinetuningConfig(AudioPretrainingConfig):
         default=False, metadata={"help": "evaluation with BLEU scores"}
     )
     eval_bleu_detok: Optional[str] = field(
-        default=None, metadata={
+        default=None,
+        metadata={
             "help": "detokenize before computing BLEU (e.g., 'moses'); "
-                    "required if using --eval-bleu; use 'space' to disable "
-                    "detokenization; see fairseq.data.encoders for other options"
-        }
+            "required if using --eval-bleu; use 'space' to disable "
+            "detokenization; see fairseq.data.encoders for other options"
+        },
     )
     eval_bleu_detok_args: str = field(
-        default="{}",
-        metadata={"help": "args for building the tokenizer, if needed"}
+        default="{}", metadata={"help": "args for building the tokenizer, if needed"}
     )
     eval_tokenized_bleu: bool = field(
-        default=False,
-        metadata={"help": "compute tokenized BLEU instead of sacrebleu"}
+        default=False, metadata={"help": "compute tokenized BLEU instead of sacrebleu"}
     )
     eval_bleu_remove_bpe: Optional[str] = field(
         default=None, metadata={"help": "remove BPE before computing BLEU"}
     )
     eval_bleu_args: str = field(
         default="{}",
-        metadata={"help": "generation args for BLUE scoring, e.g., "
-                          "'{\"beam\": 4, \"lenpen\": 0.6}'"}
+        metadata={
+            "help": "generation args for BLUE scoring, e.g., "
+            '\'{"beam": 4, "lenpen": 0.6}\''
+        },
     )
     eval_bleu_print_samples: bool = field(
-        default=False,
-        metadata={"help": "print sample generations during validation"}
+        default=False, metadata={"help": "print sample generations during validation"}
     )
     autoregressive: bool = field(
         default=False,
@@ -123,7 +123,9 @@ class AudioFinetuningTask(AudioPretrainingTask):
             return Dictionary.load(dict_path)
         return None
 
-    def load_dataset(self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs):
+    def load_dataset(
+        self, split: str, task_cfg: AudioFinetuningConfig = None, **kwargs
+    ):
         super().load_dataset(split, task_cfg, **kwargs)
 
         task_cfg = task_cfg or self.cfg
@@ -138,7 +140,8 @@ class AudioFinetuningTask(AudioPretrainingTask):
         with open(label_path, "r") as f:
             labels = [
                 text_compressor.compress(l)
-                for i, l in enumerate(f) if i not in skipped_indices
+                for i, l in enumerate(f)
+                if i not in skipped_indices
             ]
 
         assert len(labels) == len(self.datasets[split]), (
@@ -157,7 +160,7 @@ class AudioFinetuningTask(AudioPretrainingTask):
             process_label=process_label,
             label_len_fn=label_len_fn,
             add_to_input=task_cfg.get("autoregressive", False),
-            text_compression_level=text_compression_level
+            text_compression_level=text_compression_level,
         )
 
     @property
@@ -176,8 +179,8 @@ class AudioFinetuningTask(AudioPretrainingTask):
             logging_output["_num_words"] = metrics["num_words"]
         if self.cfg.eval_bleu and self.cfg.autoregressive:
             metrics = self._inference_with_bleu(self.sequence_generator, sample, model)
-            logging_output['_bleu_sys_len'] = metrics.sys_len
-            logging_output['_bleu_ref_len'] = metrics.ref_len
+            logging_output["_bleu_sys_len"] = metrics.sys_len
+            logging_output["_bleu_ref_len"] = metrics.ref_len
             # we split counts into separate entries so that they can be
             # summed efficiently across workers using fast-stat-sync
             assert len(metrics.counts) == 4
@@ -200,9 +203,9 @@ class AudioFinetuningTask(AudioPretrainingTask):
                 self.tokenizer = None
         if self.cfg.eval_bleu and self.cfg.autoregressive:
             assert self.cfg.eval_bleu_detok is not None, (
-                '--eval-bleu-detok is required if using --eval-bleu; '
-                'try --eval-bleu-detok=moses (or --eval-bleu-detok=space '
-                'to disable detokenization, e.g., when using sentencepiece)'
+                "--eval-bleu-detok is required if using --eval-bleu; "
+                "try --eval-bleu-detok=moses (or --eval-bleu-detok=space "
+                "to disable detokenization, e.g., when using sentencepiece)"
             )
             detok_args = json.loads(self.cfg.eval_bleu_detok_args)
             self.tokenizer = encoders.build_tokenizer(
@@ -261,9 +264,7 @@ class AudioFinetuningTask(AudioPretrainingTask):
                 # BLEU scores. Instead, we use a somewhat more verbose
                 # alternative that is unlikely to appear in the real
                 # reference, but doesn't get split into multiple tokens.
-                unk_string=(
-                    "UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP"
-                ),
+                unk_string=("UNKNOWNTOKENINREF" if is_ref else "UNKNOWNTOKENINHYP"),
             )
             if self.tokenizer:
                 s = self.tokenizer.decode(s)
@@ -272,21 +273,18 @@ class AudioFinetuningTask(AudioPretrainingTask):
         gen_out = self.inference_step(generator, [model], sample)
         hyps, refs = [], []
         for i in range(len(gen_out)):
-            hyps.append(decode(gen_out[i][0]['tokens'], is_ref=False))
+            hyps.append(decode(gen_out[i][0]["tokens"], is_ref=False))
             refs.append(
                 decode(
-                    utils.strip_pad(
-                        sample['target'][i],
-                        self.target_dictionary.pad()
-                    ),
+                    utils.strip_pad(sample["target"][i], self.target_dictionary.pad()),
                     is_ref=True,  # don't count <unk> as matches to the hypo
                 )
             )
         if self.cfg.eval_bleu_print_samples:
-            logger.info('H-{} {}'.format(sample["id"][0], hyps[0]))
-            logger.info('T-{} {}'.format(sample["id"][0], refs[0]))
+            logger.info("H-{} {}".format(sample["id"][0], hyps[0]))
+            logger.info("T-{} {}".format(sample["id"][0], refs[0]))
 
-        eval_tokenization = 'none' if self.cfg.eval_tokenized_bleu else '13a'
+        eval_tokenization = "none" if self.cfg.eval_tokenized_bleu else "13a"
         return sacrebleu.corpus_bleu(hyps, [refs], tokenize=eval_tokenization)
 
     def reduce_metrics(self, logging_outputs, criterion):
@@ -329,18 +327,17 @@ class AudioFinetuningTask(AudioPretrainingTask):
             count_keys = [f"_bleu_counts_{i}" for i in range(4)]
             total_keys = [f"_bleu_totals_{i}" for i in range(4)]
             for k in len_keys + count_keys + total_keys:
-                metrics.log_scalar(
-                    k, sum(log.get(k, 0) for log in logging_outputs)
-                )
+                metrics.log_scalar(k, sum(log.get(k, 0) for log in logging_outputs))
 
             import sacrebleu
+
             metrics.log_derived(
-                'bleu',
+                "bleu",
                 lambda meters: sacrebleu.compute_bleu(
                     correct=[meters[k].sum for k in count_keys],
                     total=[meters[k].sum for k in total_keys],
-                    sys_len=meters['_bleu_sys_len'].sum,
-                    ref_len=meters['_bleu_ref_len'].sum,
-                    smooth_method="exp"
-                ).score
+                    sys_len=meters["_bleu_sys_len"].sum,
+                    ref_len=meters["_bleu_ref_len"].sum,
+                    smooth_method="exp",
+                ).score,
             )

fairseq/tasks/audio_pretraining.py

@@ -50,8 +50,7 @@ class AudioPretrainingConfig(FairseqDataclass):
     data: str = field(default=MISSING, metadata={"help": "path to data directory"})
     labels: Optional[str] = field(
         default=None,
-        metadata={
-            "help": "extension of the label file to load, used for fine-tuning"},
+        metadata={"help": "extension of the label file to load, used for fine-tuning"},
     )
     binarized_dataset: bool = field(
         default=False,
@@ -102,8 +101,8 @@ class AudioPretrainingConfig(FairseqDataclass):
         default="none",
         metadata={
             "help": "compression level for texts (e.g. audio filenames, "
-                    "target texts): none/low/high (default: none). "
-        }
+            "target texts): none/low/high (default: none). "
+        },
     )
 
 

fairseq/tasks/denoising.py

@@ -135,7 +135,6 @@ class DenoisingTask(LegacyFairseqTask):
             'e.g., "train,valid" (default: all dataset splits)',
         )
 
-
     def __init__(self, args, dictionary):
         super().__init__(args)
         self.dictionary = dictionary

fairseq/tasks/frm_text_to_speech.py

@@ -11,20 +11,19 @@ from fairseq.tasks.text_to_speech import TextToSpeechTask
 
 
 logging.basicConfig(
-        format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
-        datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO
+    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    level=logging.INFO,
 )
 logger = logging.getLogger(__name__)
 
 
-@register_task('frm_text_to_speech')
+@register_task("frm_text_to_speech")
 class FrmTextToSpeechTask(TextToSpeechTask):
     @staticmethod
     def add_args(parser):
         TextToSpeechTask.add_args(parser)
-        parser.add_argument(
-            "--do_chunk", action="store_true", help="train on chunks"
-        )
+        parser.add_argument("--do_chunk", action="store_true", help="train on chunks")
         parser.add_argument("--chunk_bound", default=-1, type=int)
         parser.add_argument("--chunk_init", default=50, type=int)
         parser.add_argument("--chunk_incr", default=5, type=int)
@@ -52,5 +51,5 @@ class FrmTextToSpeechTask(TextToSpeechTask):
             chunk_incr=self.args.chunk_incr,
             add_eos=self.args.add_eos,
             dedup=self.args.dedup,
-            ref_fpu=self.args.ref_fpu
+            ref_fpu=self.args.ref_fpu,
         )