fix multigpu training test=asr

paddlespeech/s2t/models/u2/u2.py

@@ -605,8 +605,8 @@ class U2BaseModel(ASRInterface, nn.Layer):
             xs: paddle.Tensor,
             offset: int,
             required_cache_size: int,
-            att_cache: paddle.Tensor,
-            cnn_cache: paddle.Tensor,
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """ Export interface for c++ call, give input chunk xs, and return
             output from time 0 to current chunk.

paddlespeech/s2t/modules/attention.py

@@ -86,7 +86,8 @@ class MultiHeadedAttention(nn.Layer):
             self,
             value: paddle.Tensor,
             scores: paddle.Tensor,
-            mask: paddle.Tensor, ) -> paddle.Tensor:
+            mask: paddle.Tensor,  # paddle.ones([0, 0, 0], dtype=paddle.bool)
+    ) -> paddle.Tensor:
         """Compute attention context vector.
         Args:
             value (paddle.Tensor): Transformed value, size
@@ -126,13 +127,15 @@ class MultiHeadedAttention(nn.Layer):
 
         return self.linear_out(x)  # (batch, time1, d_model)
 
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                mask: paddle.Tensor,
-                pos_emb: paddle.Tensor,
-                cache: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            query: paddle.Tensor,
+            key: paddle.Tensor,
+            value: paddle.Tensor,
+            mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            pos_emb: paddle.Tensor,  # paddle.empty([0])
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute scaled dot product attention.
        Args:
             query (paddle.Tensor): Query tensor (#batch, time1, size).
@@ -241,13 +244,15 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
 
         return x
 
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                mask: paddle.Tensor,
-                pos_emb: paddle.Tensor,
-                cache: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            query: paddle.Tensor,
+            key: paddle.Tensor,
+            value: paddle.Tensor,
+            mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            pos_emb: paddle.Tensor,  # paddle.empty([0])
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
         Args:
             query (paddle.Tensor): Query tensor (#batch, time1, size).

paddlespeech/s2t/modules/conformer_convolution.py

@@ -105,10 +105,12 @@ class ConvolutionModule(nn.Layer):
         )
         self.activation = activation
 
-    def forward(self,
-                x: paddle.Tensor,
-                mask_pad: paddle.Tensor,
-                cache: paddle.Tensor) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            x: paddle.Tensor,
+            mask_pad: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute convolution module.
         Args:
             x (paddle.Tensor): Input tensor (#batch, time, channels).

paddlespeech/s2t/modules/encoder.py

@@ -190,9 +190,9 @@ class BaseEncoder(nn.Layer):
             xs: paddle.Tensor,
             offset: int,
             required_cache_size: int,
-            att_cache: paddle.Tensor,
-            cnn_cache: paddle.Tensor,
-            att_mask: paddle.Tensor,
+            att_cache: paddle.Tensor,  # paddle.zeros([0,0,0,0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0,0,0,0]),
+            att_mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """ Forward just one chunk
         Args:

paddlespeech/s2t/modules/encoder_layer.py

@@ -76,9 +76,10 @@ class TransformerEncoderLayer(nn.Layer):
             x: paddle.Tensor,
             mask: paddle.Tensor,
             pos_emb: paddle.Tensor,
-            mask_pad: paddle.Tensor,
-            att_cache: paddle.Tensor,
-            cnn_cache: paddle.Tensor,
+            mask_pad: paddle.
+            Tensor,  # paddle.ones([0, 0, 0], dtype=paddle.bool)
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """Compute encoded features.
         Args:
@@ -194,9 +195,10 @@ class ConformerEncoderLayer(nn.Layer):
             x: paddle.Tensor,
             mask: paddle.Tensor,
             pos_emb: paddle.Tensor,
-            mask_pad: paddle.Tensor,
-            att_cache: paddle.Tensor,
-            cnn_cache: paddle.Tensor,
+            mask_pad: paddle.
+            Tensor,  # paddle.ones([0, 0, 0], dtype=paddle.bool)
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """Compute encoded features.
         Args: