From 3cd71a385a31f987f2db99df902ca36ee02b1813 Mon Sep 17 00:00:00 2001
From: 九耳 <mengzhe.cmz@alibaba-inc.com>
Date: 星期四, 30 三月 2023 17:29:12 +0800
Subject: [PATCH] change name

---
 funasr/export/models/encoder/sanm_encoder.py |   26 ++++----------------------
 1 files changed, 4 insertions(+), 22 deletions(-)

diff --git a/funasr/export/models/encoder/sanm_encoder.py b/funasr/export/models/encoder/sanm_encoder.py
index 118e240..8390f68 100644
--- a/funasr/export/models/encoder/sanm_encoder.py
+++ b/funasr/export/models/encoder/sanm_encoder.py
@@ -9,20 +9,6 @@
 from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
 from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export
 
-def subsequent_mask(size, device="cpu", dtype=torch.bool):
-    """Create mask for subsequent steps (size, size).
-
-    :param int size: size of mask
-    :param str device: "cpu" or "cuda" or torch.Tensor.device
-    :param torch.dtype dtype: result dtype
-    :rtype: torch.Tensor
-    >>> subsequent_mask(3)
-    [[1, 0, 0],
-     [1, 1, 0],
-     [1, 1, 1]]
-    """
-    ret = torch.ones(size, size, device=device, dtype=dtype)
-    return torch.tril(ret, out=ret)
 
 class SANMEncoder(nn.Module):
     def __init__(
@@ -163,14 +149,9 @@
         self.num_heads = model.encoders[0].self_attn.h
         self.hidden_size = model.encoders[0].self_attn.linear_out.out_features
     
-    def prepare_mask(self, mask):
+    def prepare_mask(self, mask, sub_masks):
         mask_3d_btd = mask[:, :, None]
-        sub_masks = subsequent_mask(mask.size(-1))
-        if len(mask.shape) == 2:
-            mask_4d_bhlt = 1 - sub_masks[:, None, None, :]
-        elif len(mask.shape) == 3:
-            mask_4d_bhlt = 1 - sub_masks[:, None, :]
-        mask_4d_bhlt = mask_4d_bhlt * -10000.0
+        mask_4d_bhlt = (1 - sub_masks) * -10000.0
         
         return mask_3d_btd, mask_4d_bhlt
     
@@ -178,10 +159,11 @@
                 speech: torch.Tensor,
                 speech_lengths: torch.Tensor,
                 vad_mask: torch.Tensor,
+                sub_masks: torch.Tensor,
                 ):
         speech = speech * self._output_size ** 0.5
         mask = self.make_pad_mask(speech_lengths)
-        mask = self.prepare_mask(mask)
+        mask = self.prepare_mask(mask, sub_masks)
         if self.embed is None:
             xs_pad = speech
         else:

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