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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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from typing import Sequence
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import paddle
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from paddle import distributed as dist
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from parakeet.models.transformer_tts import GuidedMultiHeadAttentionLoss
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from parakeet.models.transformer_tts import TransformerTTSLoss
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from parakeet.training.extensions.evaluator import StandardEvaluator
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from parakeet.training.reporter import report
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from parakeet.training.updaters.standard_updater import StandardUpdater
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logging.basicConfig(
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format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
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datefmt='[%Y-%m-%d %H:%M:%S]')
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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class TransformerTTSUpdater(StandardUpdater):
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def __init__(
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self,
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model,
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optimizer,
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dataloader,
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init_state=None,
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use_masking=False,
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use_weighted_masking=False,
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output_dir=None,
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bce_pos_weight=5.0,
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loss_type: str="L1",
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use_guided_attn_loss: bool=True,
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modules_applied_guided_attn: Sequence[str]=("encoder-decoder"),
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guided_attn_loss_sigma: float=0.4,
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guided_attn_loss_lambda: float=1.0, ):
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super().__init__(model, optimizer, dataloader, init_state=None)
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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self.bce_pos_weight = bce_pos_weight
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self.loss_type = loss_type
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self.use_guided_attn_loss = use_guided_attn_loss
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self.guided_attn_loss_sigma = guided_attn_loss_sigma
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self.guided_attn_loss_lambda = guided_attn_loss_lambda
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self.modules_applied_guided_attn = modules_applied_guided_attn
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self.criterion = TransformerTTSLoss(
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use_masking=self.use_masking,
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use_weighted_masking=self.use_weighted_masking,
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bce_pos_weight=self.bce_pos_weight)
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if self.use_guided_attn_loss:
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self.attn_criterion = GuidedMultiHeadAttentionLoss(
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sigma=self.guided_attn_loss_sigma,
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alpha=self.guided_attn_loss_lambda, )
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log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
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self.filehandler = logging.FileHandler(str(log_file))
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logger.addHandler(self.filehandler)
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self.logger = logger
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self.msg = ""
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def update_core(self, batch):
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self.msg = "Rank: {}, ".format(dist.get_rank())
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losses_dict = {}
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after_outs, before_outs, logits, ys, labels, olens, ilens, need_dict = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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speech=batch["speech"],
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speech_lengths=batch["speech_lengths"], )
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l1_loss, l2_loss, bce_loss = self.criterion(
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after_outs=after_outs,
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before_outs=before_outs,
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logits=logits,
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ys=ys,
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labels=labels,
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olens=olens)
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report("train/bce_loss", float(bce_loss))
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report("train/l1_loss", float(l1_loss))
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report("train/l2_loss", float(l2_loss))
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losses_dict["bce_loss"] = float(bce_loss)
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losses_dict["l1_loss"] = float(l1_loss)
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losses_dict["l2_loss"] = float(l2_loss)
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# caluculate loss values
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if self.loss_type == "L1":
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loss = l1_loss + bce_loss
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elif self.loss_type == "L2":
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loss = l2_loss + bce_loss
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elif self.loss_type == "L1+L2":
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loss = l1_loss + l2_loss + bce_loss
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else:
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raise ValueError("unknown --loss-type " + self.loss_type)
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# calculate guided attention loss
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if self.use_guided_attn_loss:
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# calculate for encoder
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if "encoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['encoder'].encoders)))):
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att_ws += [
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need_dict['encoder'].encoders[layer_idx].self_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_in, T_in)
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att_ws = paddle.concat(att_ws, axis=1)
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enc_attn_loss = self.attn_criterion(att_ws, ilens, ilens)
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loss = loss + enc_attn_loss
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report("train/enc_attn_loss", float(enc_attn_loss))
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losses_dict["enc_attn_loss"] = float(enc_attn_loss)
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# calculate for decoder
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if "decoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['decoder'].decoders)))):
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att_ws += [
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need_dict['decoder'].decoders[layer_idx].self_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_out, T_out)
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att_ws = paddle.concat(att_ws, axis=1)
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dec_attn_loss = self.attn_criterion(att_ws, olens, olens)
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report("train/dec_attn_loss", float(dec_attn_loss))
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losses_dict["dec_attn_loss"] = float(dec_attn_loss)
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loss = loss + dec_attn_loss
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# calculate for encoder-decoder
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if "encoder-decoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['decoder'].decoders)))):
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att_ws += [
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need_dict['decoder'].decoders[layer_idx].src_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_out, T_in)
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att_ws = paddle.concat(att_ws, axis=1)
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enc_dec_attn_loss = self.attn_criterion(att_ws, ilens, olens)
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report("train/enc_dec_attn_loss", float(enc_dec_attn_loss))
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losses_dict["enc_dec_attn_loss"] = float(enc_dec_attn_loss)
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loss = loss + enc_dec_attn_loss
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if need_dict['use_scaled_pos_enc']:
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report("train/encoder_alpha",
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float(need_dict['encoder'].embed[-1].alpha))
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report("train/decoder_alpha",
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float(need_dict['decoder'].embed[-1].alpha))
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losses_dict["encoder_alpha"] = float(
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need_dict['encoder'].embed[-1].alpha)
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losses_dict["decoder_alpha"] = float(
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need_dict['decoder'].embed[-1].alpha)
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optimizer = self.optimizer
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optimizer.clear_grad()
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loss.backward()
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optimizer.step()
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report("train/loss", float(loss))
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losses_dict["loss"] = float(loss)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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class TransformerTTSEvaluator(StandardEvaluator):
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def __init__(
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self,
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model,
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dataloader,
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init_state=None,
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use_masking=False,
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use_weighted_masking=False,
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output_dir=None,
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bce_pos_weight=5.0,
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loss_type: str="L1",
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use_guided_attn_loss: bool=True,
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modules_applied_guided_attn: Sequence[str]=("encoder-decoder"),
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guided_attn_loss_sigma: float=0.4,
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guided_attn_loss_lambda: float=1.0, ):
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super().__init__(model, dataloader)
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self.use_masking = use_masking
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self.use_weighted_masking = use_weighted_masking
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self.bce_pos_weight = bce_pos_weight
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self.loss_type = loss_type
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self.use_guided_attn_loss = use_guided_attn_loss
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self.guided_attn_loss_sigma = guided_attn_loss_sigma
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self.guided_attn_loss_lambda = guided_attn_loss_lambda
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self.modules_applied_guided_attn = modules_applied_guided_attn
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self.criterion = TransformerTTSLoss(
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use_masking=self.use_masking,
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use_weighted_masking=self.use_weighted_masking,
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bce_pos_weight=self.bce_pos_weight)
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if self.use_guided_attn_loss:
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self.attn_criterion = GuidedMultiHeadAttentionLoss(
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sigma=self.guided_attn_loss_sigma,
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alpha=self.guided_attn_loss_lambda, )
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log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
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self.filehandler = logging.FileHandler(str(log_file))
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logger.addHandler(self.filehandler)
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self.logger = logger
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self.msg = ""
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def evaluate_core(self, batch):
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self.msg = "Evaluate: "
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losses_dict = {}
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after_outs, before_outs, logits, ys, labels, olens, ilens, need_dict = self.model(
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text=batch["text"],
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text_lengths=batch["text_lengths"],
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speech=batch["speech"],
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speech_lengths=batch["speech_lengths"])
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l1_loss, l2_loss, bce_loss = self.criterion(
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after_outs=after_outs,
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before_outs=before_outs,
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logits=logits,
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ys=ys,
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labels=labels,
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olens=olens)
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report("eval/bce_loss", float(bce_loss))
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report("eval/l1_loss", float(l1_loss))
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report("eval/l2_loss", float(l2_loss))
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losses_dict["bce_loss"] = float(bce_loss)
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losses_dict["l1_loss"] = float(l1_loss)
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losses_dict["l2_loss"] = float(l2_loss)
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# caluculate loss values
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if self.loss_type == "L1":
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loss = l1_loss + bce_loss
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elif self.loss_type == "L2":
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loss = l2_loss + bce_loss
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elif self.loss_type == "L1+L2":
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loss = l1_loss + l2_loss + bce_loss
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else:
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raise ValueError("unknown --loss-type " + self.loss_type)
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# calculate guided attention loss
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if self.use_guided_attn_loss:
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# calculate for encoder
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if "encoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['encoder'].encoders)))):
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att_ws += [
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need_dict['encoder'].encoders[layer_idx].self_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_in, T_in)
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att_ws = paddle.concat(att_ws, axis=1)
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enc_attn_loss = self.attn_criterion(att_ws, ilens, ilens)
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loss = loss + enc_attn_loss
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report("train/enc_attn_loss", float(enc_attn_loss))
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losses_dict["enc_attn_loss"] = float(enc_attn_loss)
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# calculate for decoder
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if "decoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['decoder'].decoders)))):
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att_ws += [
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need_dict['decoder'].decoders[layer_idx].self_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_out, T_out)
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att_ws = paddle.concat(att_ws, axis=1)
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dec_attn_loss = self.attn_criterion(att_ws, olens, olens)
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report("eval/dec_attn_loss", float(dec_attn_loss))
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losses_dict["dec_attn_loss"] = float(dec_attn_loss)
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loss = loss + dec_attn_loss
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# calculate for encoder-decoder
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if "encoder-decoder" in self.modules_applied_guided_attn:
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att_ws = []
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for idx, layer_idx in enumerate(
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reversed(range(len(need_dict['decoder'].decoders)))):
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att_ws += [
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need_dict['decoder'].decoders[layer_idx].src_attn.
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attn[:, :need_dict['num_heads_applied_guided_attn']]
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]
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if idx + 1 == need_dict['num_layers_applied_guided_attn']:
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break
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# (B, H*L, T_out, T_in)
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att_ws = paddle.concat(att_ws, axis=1)
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enc_dec_attn_loss = self.attn_criterion(att_ws, ilens, olens)
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report("eval/enc_dec_attn_loss", float(enc_dec_attn_loss))
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losses_dict["enc_dec_attn_loss"] = float(enc_dec_attn_loss)
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loss = loss + enc_dec_attn_loss
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if need_dict['use_scaled_pos_enc']:
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report("eval/encoder_alpha",
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float(need_dict['encoder'].embed[-1].alpha))
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report("eval/decoder_alpha",
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float(need_dict['decoder'].embed[-1].alpha))
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losses_dict["encoder_alpha"] = float(
|
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need_dict['encoder'].embed[-1].alpha)
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losses_dict["decoder_alpha"] = float(
|
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need_dict['decoder'].embed[-1].alpha)
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report("eval/loss", float(loss))
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losses_dict["loss"] = float(loss)
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self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_dict.items())
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self.logger.info(self.msg)
|
