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@@ -5,7 +5,18 @@ from typing import Tuple
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import torch
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from funasr.modules.scorers.scorer_interface import BatchScorerInterface
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+from typing import Dict
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+from typing import Optional
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+from typing import Tuple
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+
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+import torch
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+import torch.nn.functional as F
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+from typeguard import check_argument_types
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+from funasr.modules.nets_utils import make_pad_mask
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+from funasr.lm.abs_model import AbsLM
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+from funasr.torch_utils.device_funcs import force_gatherable
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+from funasr.train.abs_espnet_model import AbsESPnetModel
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class AbsLM(torch.nn.Module, BatchScorerInterface, ABC):
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"""The abstract LM class
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@@ -27,3 +38,122 @@ class AbsLM(torch.nn.Module, BatchScorerInterface, ABC):
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self, input: torch.Tensor, hidden: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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raise NotImplementedError
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+
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+
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+class LanguageModel(AbsESPnetModel):
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+ def __init__(self, lm: AbsLM, vocab_size: int, ignore_id: int = 0):
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+ assert check_argument_types()
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+ super().__init__()
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+ self.lm = lm
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+ self.sos = 1
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+ self.eos = 2
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+
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+ # ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
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+ self.ignore_id = ignore_id
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+
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+ def nll(
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+ self,
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+ text: torch.Tensor,
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+ text_lengths: torch.Tensor,
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+ max_length: Optional[int] = None,
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+ ) -> Tuple[torch.Tensor, torch.Tensor]:
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+ """Compute negative log likelihood(nll)
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+
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+ Normally, this function is called in batchify_nll.
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+ Args:
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+ text: (Batch, Length)
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+ text_lengths: (Batch,)
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+ max_lengths: int
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+ """
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+ batch_size = text.size(0)
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+ # For data parallel
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+ if max_length is None:
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+ text = text[:, : text_lengths.max()]
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+ else:
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+ text = text[:, :max_length]
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+
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+ # 1. Create a sentence pair like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
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+ # text: (Batch, Length) -> x, y: (Batch, Length + 1)
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+ x = F.pad(text, [1, 0], "constant", self.sos)
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+ t = F.pad(text, [0, 1], "constant", self.ignore_id)
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+ for i, l in enumerate(text_lengths):
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+ t[i, l] = self.eos
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+ x_lengths = text_lengths + 1
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+
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+ # 2. Forward Language model
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+ # x: (Batch, Length) -> y: (Batch, Length, NVocab)
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+ y, _ = self.lm(x, None)
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+
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+ # 3. Calc negative log likelihood
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+ # nll: (BxL,)
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+ nll = F.cross_entropy(y.view(-1, y.shape[-1]), t.view(-1), reduction="none")
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+ # nll: (BxL,) -> (BxL,)
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+ if max_length is None:
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+ nll.masked_fill_(make_pad_mask(x_lengths).to(nll.device).view(-1), 0.0)
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+ else:
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+ nll.masked_fill_(
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+ make_pad_mask(x_lengths, maxlen=max_length + 1).to(nll.device).view(-1),
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+ 0.0,
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+ )
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+ # nll: (BxL,) -> (B, L)
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+ nll = nll.view(batch_size, -1)
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+ return nll, x_lengths
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+
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+ def batchify_nll(
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+ self, text: torch.Tensor, text_lengths: torch.Tensor, batch_size: int = 100
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+ ) -> Tuple[torch.Tensor, torch.Tensor]:
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+ """Compute negative log likelihood(nll) from transformer language model
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+
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+ To avoid OOM, this fuction seperate the input into batches.
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+ Then call nll for each batch and combine and return results.
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+ Args:
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+ text: (Batch, Length)
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+ text_lengths: (Batch,)
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+ batch_size: int, samples each batch contain when computing nll,
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+ you may change this to avoid OOM or increase
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+
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+ """
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+ total_num = text.size(0)
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+ if total_num <= batch_size:
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+ nll, x_lengths = self.nll(text, text_lengths)
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+ else:
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+ nlls = []
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+ x_lengths = []
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+ max_length = text_lengths.max()
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+
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+ start_idx = 0
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+ while True:
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+ end_idx = min(start_idx + batch_size, total_num)
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+ batch_text = text[start_idx:end_idx, :]
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+ batch_text_lengths = text_lengths[start_idx:end_idx]
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+ # batch_nll: [B * T]
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+ batch_nll, batch_x_lengths = self.nll(
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+ batch_text, batch_text_lengths, max_length=max_length
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+ )
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+ nlls.append(batch_nll)
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+ x_lengths.append(batch_x_lengths)
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+ start_idx = end_idx
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+ if start_idx == total_num:
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+ break
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+ nll = torch.cat(nlls)
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+ x_lengths = torch.cat(x_lengths)
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+ assert nll.size(0) == total_num
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+ assert x_lengths.size(0) == total_num
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+ return nll, x_lengths
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+
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+ def forward(
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+ self, text: torch.Tensor, text_lengths: torch.Tensor
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+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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+ nll, y_lengths = self.nll(text, text_lengths)
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+ ntokens = y_lengths.sum()
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+ loss = nll.sum() / ntokens
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+ stats = dict(loss=loss.detach())
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+
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+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
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+ loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device)
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+ return loss, stats, weight
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+
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+ def collect_feats(
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+ self, text: torch.Tensor, text_lengths: torch.Tensor
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+ ) -> Dict[str, torch.Tensor]:
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+ return {}
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游雁