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261 lines
8.4 KiB
261 lines
8.4 KiB
# 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 Any
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from typing import List
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from typing import Tuple
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from deepspeech.decoders.scorers.scorer_interface import BatchScorerInterface
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from deepspeech.models.lm_interface import LMInterface
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from deepspeech.modules.encoder import TransformerEncoder
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from deepspeech.modules.mask import subsequent_mask
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class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
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def __init__(
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self,
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n_vocab: int,
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pos_enc: str=None,
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embed_unit: int=128,
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att_unit: int=256,
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head: int=2,
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unit: int=1024,
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layer: int=4,
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dropout_rate: float=0.5,
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emb_dropout_rate: float=0.0,
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att_dropout_rate: float=0.0,
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tie_weights: bool=False, ):
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nn.Layer.__init__(self)
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if pos_enc == "sinusoidal":
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pos_enc_layer_type = "abs_pos"
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elif pos_enc is None:
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pos_enc_layer_type = "no_pos"
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else:
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raise ValueError(f"unknown pos-enc option: {pos_enc}")
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self.embed = nn.Embedding(n_vocab, embed_unit)
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if emb_dropout_rate == 0.0:
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self.embed_drop = None
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else:
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self.embed_drop = nn.Dropout(emb_dropout_rate)
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self.encoder = TransformerEncoder(
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input_size=embed_unit,
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output_size=att_unit,
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attention_heads=head,
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linear_units=unit,
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num_blocks=layer,
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dropout_rate=dropout_rate,
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attention_dropout_rate=att_dropout_rate,
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input_layer="linear",
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pos_enc_layer_type=pos_enc_layer_type,
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concat_after=False,
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static_chunk_size=1,
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use_dynamic_chunk=False,
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use_dynamic_left_chunk=False)
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self.decoder = nn.Linear(att_unit, n_vocab)
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logging.info("Tie weights set to {}".format(tie_weights))
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logging.info("Dropout set to {}".format(dropout_rate))
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logging.info("Emb Dropout set to {}".format(emb_dropout_rate))
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logging.info("Att Dropout set to {}".format(att_dropout_rate))
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if tie_weights:
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assert (
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att_unit == embed_unit
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), "Tie Weights: True need embedding and final dimensions to match"
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self.decoder.weight = self.embed.weight
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def _target_mask(self, ys_in_pad):
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ys_mask = ys_in_pad != 0
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m = subsequent_mask(ys_mask.size(-1)).unsqueeze(0)
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return ys_mask.unsqueeze(-2) & m
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def forward(self, x: paddle.Tensor, t: paddle.Tensor
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) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
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"""Compute LM loss value from buffer sequences.
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Args:
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x (paddle.Tensor): Input ids. (batch, len)
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t (paddle.Tensor): Target ids. (batch, len)
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Returns:
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tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]: Tuple of
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loss to backward (scalar),
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negative log-likelihood of t: -log p(t) (scalar) and
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the number of elements in x (scalar)
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Notes:
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The last two return values are used
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in perplexity: p(t)^{-n} = exp(-log p(t) / n)
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"""
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xm = x != 0
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xlen = xm.sum(axis=1)
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if self.embed_drop is not None:
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emb = self.embed_drop(self.embed(x))
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else:
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emb = self.embed(x)
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h, _ = self.encoder(emb, xlen)
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y = self.decoder(h)
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loss = F.cross_entropy(
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y.view(-1, y.shape[-1]), t.view(-1), reduction="none")
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mask = xm.to(dtype=loss.dtype)
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logp = loss * mask.view(-1)
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logp = logp.sum()
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count = mask.sum()
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return logp / count, logp, count
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# beam search API (see ScorerInterface)
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def score(self, y: paddle.Tensor, state: Any,
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x: paddle.Tensor) -> Tuple[paddle.Tensor, Any]:
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"""Score new token.
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Args:
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y (paddle.Tensor): 1D paddle.int64 prefix tokens.
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state: Scorer state for prefix tokens
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x (paddle.Tensor): encoder feature that generates ys.
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Returns:
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tuple[paddle.Tensor, Any]: Tuple of
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paddle.float32 scores for next token (n_vocab)
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and next state for ys
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"""
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y = y.unsqueeze(0)
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if self.embed_drop is not None:
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emb = self.embed_drop(self.embed(y))
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else:
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emb = self.embed(y)
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h, _, cache = self.encoder.forward_one_step(
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emb, self._target_mask(y), cache=state)
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h = self.decoder(h[:, -1])
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logp = F.log_softmax(h).squeeze(0)
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return logp, cache
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# batch beam search API (see BatchScorerInterface)
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def batch_score(self,
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ys: paddle.Tensor,
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states: List[Any],
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xs: paddle.Tensor) -> Tuple[paddle.Tensor, List[Any]]:
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"""Score new token batch (required).
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Args:
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ys (paddle.Tensor): paddle.int64 prefix tokens (n_batch, ylen).
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states (List[Any]): Scorer states for prefix tokens.
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xs (paddle.Tensor):
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The encoder feature that generates ys (n_batch, xlen, n_feat).
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Returns:
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tuple[paddle.Tensor, List[Any]]: Tuple of
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batchfied scores for next token with shape of `(n_batch, n_vocab)`
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and next state list for ys.
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"""
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# merge states
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n_batch = len(ys)
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n_layers = len(self.encoder.encoders)
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if states[0] is None:
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batch_state = None
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else:
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# transpose state of [batch, layer] into [layer, batch]
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batch_state = [
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paddle.stack([states[b][i] for b in range(n_batch)])
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for i in range(n_layers)
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]
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if self.embed_drop is not None:
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emb = self.embed_drop(self.embed(ys))
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else:
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emb = self.embed(ys)
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# batch decoding
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h, _, states = self.encoder.forward_one_step(
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emb, self._target_mask(ys), cache=batch_state)
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h = self.decoder(h[:, -1])
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logp = F.log_softmax(h)
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# transpose state of [layer, batch] into [batch, layer]
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state_list = [[states[i][b] for i in range(n_layers)]
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for b in range(n_batch)]
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return logp, state_list
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if __name__ == "__main__":
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tlm = TransformerLM(
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n_vocab=5002,
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pos_enc=None,
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embed_unit=128,
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att_unit=512,
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head=8,
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unit=2048,
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layer=16,
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dropout_rate=0.5, )
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# n_vocab: int,
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# pos_enc: str=None,
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# embed_unit: int=128,
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# att_unit: int=256,
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# head: int=2,
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# unit: int=1024,
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# layer: int=4,
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# dropout_rate: float=0.5,
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# emb_dropout_rate: float = 0.0,
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# att_dropout_rate: float = 0.0,
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# tie_weights: bool = False,):
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paddle.set_device("cpu")
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model_dict = paddle.load("transformerLM.pdparams")
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tlm.set_state_dict(model_dict)
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tlm.eval()
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#Test the score
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input2 = np.array([5])
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input2 = paddle.to_tensor(input2)
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state = None
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output, state = tlm.score(input2, state, None)
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input3 = np.array([5, 10])
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input3 = paddle.to_tensor(input3)
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output, state = tlm.score(input3, state, None)
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input4 = np.array([5, 10, 0])
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input4 = paddle.to_tensor(input4)
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output, state = tlm.score(input4, state, None)
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print("output", output)
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"""
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#Test the batch score
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batch_size = 2
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inp2 = np.array([[5], [10]])
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inp2 = paddle.to_tensor(inp2)
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output, states = tlm.batch_score(
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inp2, [(None,None,0)] * batch_size)
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inp3 = np.array([[100], [30]])
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inp3 = paddle.to_tensor(inp3)
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output, states = tlm.batch_score(
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inp3, states)
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print("output", output)
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#print("cache", cache)
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#np.save("output_pd.npy", output)
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"""
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