From 4ba1011b42e041ee1d71448eefd7ef2e7bd61bb6 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 31 三月 2023 15:31:26 +0800
Subject: [PATCH] export
---
funasr/lm/abs_model.py | 130 +++++++++++++++++++++++++++++++++++++++++++
1 files changed, 130 insertions(+), 0 deletions(-)
diff --git a/funasr/lm/abs_model.py b/funasr/lm/abs_model.py
index 0ad1e71..997aad9 100644
--- a/funasr/lm/abs_model.py
+++ b/funasr/lm/abs_model.py
@@ -5,7 +5,18 @@
import torch
from funasr.modules.scorers.scorer_interface import BatchScorerInterface
+from typing import Dict
+from typing import Optional
+from typing import Tuple
+import torch
+import torch.nn.functional as F
+from typeguard import check_argument_types
+
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.lm.abs_model import AbsLM
+from funasr.torch_utils.device_funcs import force_gatherable
+from funasr.train.abs_espnet_model import AbsESPnetModel
class AbsLM(torch.nn.Module, BatchScorerInterface, ABC):
"""The abstract LM class
@@ -27,3 +38,122 @@
self, input: torch.Tensor, hidden: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
raise NotImplementedError
+
+
+class LanguageModel(AbsESPnetModel):
+ def __init__(self, lm: AbsLM, vocab_size: int, ignore_id: int = 0):
+ assert check_argument_types()
+ super().__init__()
+ self.lm = lm
+ self.sos = 1
+ self.eos = 2
+
+ # ignore_id may be assumed as 0, shared with CTC-blank symbol for ASR.
+ self.ignore_id = ignore_id
+
+ def nll(
+ self,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
+ max_length: Optional[int] = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute negative log likelihood(nll)
+
+ Normally, this function is called in batchify_nll.
+ Args:
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ max_lengths: int
+ """
+ batch_size = text.size(0)
+ # For data parallel
+ if max_length is None:
+ text = text[:, : text_lengths.max()]
+ else:
+ text = text[:, :max_length]
+
+ # 1. Create a sentence pair like '<sos> w1 w2 w3' and 'w1 w2 w3 <eos>'
+ # text: (Batch, Length) -> x, y: (Batch, Length + 1)
+ x = F.pad(text, [1, 0], "constant", self.sos)
+ t = F.pad(text, [0, 1], "constant", self.ignore_id)
+ for i, l in enumerate(text_lengths):
+ t[i, l] = self.eos
+ x_lengths = text_lengths + 1
+
+ # 2. Forward Language model
+ # x: (Batch, Length) -> y: (Batch, Length, NVocab)
+ y, _ = self.lm(x, None)
+
+ # 3. Calc negative log likelihood
+ # nll: (BxL,)
+ nll = F.cross_entropy(y.view(-1, y.shape[-1]), t.view(-1), reduction="none")
+ # nll: (BxL,) -> (BxL,)
+ if max_length is None:
+ nll.masked_fill_(make_pad_mask(x_lengths).to(nll.device).view(-1), 0.0)
+ else:
+ nll.masked_fill_(
+ make_pad_mask(x_lengths, maxlen=max_length + 1).to(nll.device).view(-1),
+ 0.0,
+ )
+ # nll: (BxL,) -> (B, L)
+ nll = nll.view(batch_size, -1)
+ return nll, x_lengths
+
+ def batchify_nll(
+ self, text: torch.Tensor, text_lengths: torch.Tensor, batch_size: int = 100
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Compute negative log likelihood(nll) from transformer language model
+
+ To avoid OOM, this fuction seperate the input into batches.
+ Then call nll for each batch and combine and return results.
+ Args:
+ text: (Batch, Length)
+ text_lengths: (Batch,)
+ batch_size: int, samples each batch contain when computing nll,
+ you may change this to avoid OOM or increase
+
+ """
+ total_num = text.size(0)
+ if total_num <= batch_size:
+ nll, x_lengths = self.nll(text, text_lengths)
+ else:
+ nlls = []
+ x_lengths = []
+ max_length = text_lengths.max()
+
+ start_idx = 0
+ while True:
+ end_idx = min(start_idx + batch_size, total_num)
+ batch_text = text[start_idx:end_idx, :]
+ batch_text_lengths = text_lengths[start_idx:end_idx]
+ # batch_nll: [B * T]
+ batch_nll, batch_x_lengths = self.nll(
+ batch_text, batch_text_lengths, max_length=max_length
+ )
+ nlls.append(batch_nll)
+ x_lengths.append(batch_x_lengths)
+ start_idx = end_idx
+ if start_idx == total_num:
+ break
+ nll = torch.cat(nlls)
+ x_lengths = torch.cat(x_lengths)
+ assert nll.size(0) == total_num
+ assert x_lengths.size(0) == total_num
+ return nll, x_lengths
+
+ def forward(
+ self, text: torch.Tensor, text_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ nll, y_lengths = self.nll(text, text_lengths)
+ ntokens = y_lengths.sum()
+ loss = nll.sum() / ntokens
+ stats = dict(loss=loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ loss, stats, weight = force_gatherable((loss, stats, ntokens), loss.device)
+ return loss, stats, weight
+
+ def collect_feats(
+ self, text: torch.Tensor, text_lengths: torch.Tensor
+ ) -> Dict[str, torch.Tensor]:
+ return {}
--
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