[TTS]fix dygraph to static for tacotron2, test=doc (#2426)

* fix dygraph to static for tacotron2, test=doc

* Fix dy2st error for taco2

* Update attentions.py

---------

Co-authored-by: 0x45f <wangzhen45@baidu.com>

paddlespeech/t2s/modules/tacotron2/attentions.py

@@ -47,11 +47,13 @@ def _apply_attention_constraint(e,
         https://arxiv.org/abs/1710.07654
 
     """
-    if paddle.shape(e)[0] != 1:
+    # for dygraph to static graph
-        raise NotImplementedError(
+    # if e.shape[0] != 1:
-            "Batch attention constraining is not yet supported.")
+    #     raise NotImplementedError(
-    backward_idx = last_attended_idx - backward_window
+    #         "Batch attention constraining is not yet supported.")
-    forward_idx = last_attended_idx + forward_window
+    backward_idx = paddle.cast(
+        last_attended_idx - backward_window, dtype='int64')
+    forward_idx = paddle.cast(last_attended_idx + forward_window, dtype='int64')
     if backward_idx > 0:
         e[:, :backward_idx] = -float("inf")
     if forward_idx < paddle.shape(e)[1]:
@@ -122,7 +124,7 @@ class AttLoc(nn.Layer):
             dec_z,
             att_prev,
             scaling=2.0,
-            last_attended_idx=None,
+            last_attended_idx=-1,
             backward_window=1,
             forward_window=3, ):
         """Calculate AttLoc forward propagation.
@@ -192,7 +194,7 @@ class AttLoc(nn.Layer):
 
         e = masked_fill(e, self.mask, -float("inf"))
         # apply monotonic attention constraint (mainly for TTS)
-        if last_attended_idx is not None:
+        if last_attended_idx != -1:
             e = _apply_attention_constraint(e, last_attended_idx,
                                             backward_window, forward_window)
 

paddlespeech/t2s/modules/tacotron2/decoder.py

@@ -556,13 +556,15 @@ class Decoder(nn.Layer):
         if use_att_constraint:
             last_attended_idx = 0
         else:
-            last_attended_idx = None
+            last_attended_idx = -1
 
         # loop for an output sequence
         idx = 0
         outs, att_ws, probs = [], [], []
         prob = paddle.zeros([1])
-        while True:
+        while paddle.to_tensor(True):
+            z_list = z_list
+            c_list = c_list
             # updated index
             idx += self.reduction_factor