Transfer dynamic_decode and BeamSearchDecoder to v2 op (#35656)

python/paddle/fluid/layers/rnn.py

@@ -958,7 +958,7 @@ class BeamSearchDecoder(Decoder):
         x = nn.unsqueeze(x, [1])  # [batch_size, 1, ...]
         expand_times = [1] * len(x.shape)
         expand_times[1] = beam_size
-        x = nn.expand(x, expand_times)  # [batch_size, beam_size, ...]
+        x = paddle.tile(x, expand_times)  # [batch_size, beam_size, ...]
         x = nn.transpose(x, list(range(2, len(x.shape))) +
                          [0, 1])  # [..., batch_size, beam_size]
         # use 0 to copy to avoid wrong shape
@@ -1024,7 +1024,7 @@ class BeamSearchDecoder(Decoder):
         x = nn.unsqueeze(x, [1])
         expand_times = [1] * len(x.shape)
         expand_times[1] = self.beam_size
-        x = nn.expand(x, expand_times)
+        x = paddle.tile(x, expand_times)
         return x
 
     def _mask_probs(self, probs, finished):
@@ -1050,7 +1050,7 @@ class BeamSearchDecoder(Decoder):
         # TODO: use where_op
         finished = tensor.cast(finished, dtype=probs.dtype)
         probs = nn.elementwise_mul(
-            nn.expand(nn.unsqueeze(finished, [2]), [1, 1, self.vocab_size]),
+            paddle.tile(nn.unsqueeze(finished, [2]), [1, 1, self.vocab_size]),
             self.noend_mask_tensor,
             axis=-1) - nn.elementwise_mul(
                 probs, (finished - 1), axis=0)
@@ -1080,7 +1080,7 @@ class BeamSearchDecoder(Decoder):
             batch_size,
             indices.dtype) if batch_size.dtype != indices.dtype else batch_size
         batch_size.stop_gradient = True  # TODO: remove this
-        batch_pos = nn.expand(
+        batch_pos = paddle.tile(
             nn.unsqueeze(
                 tensor.range(
                     0, batch_size, 1, dtype=indices.dtype), [1]),
@@ -1140,12 +1140,11 @@ class BeamSearchDecoder(Decoder):
 
         init_cell_states = map_structure(self._expand_to_beam_size,
                                          initial_cell_states)
-        # TODO: use fill_constant when support variable shape
-        init_inputs = nn.expand(
-            nn.unsqueeze(
-                nn.expand(self.start_token_tensor, [self.batch_size]), [1]),
-            [1, self.beam_size])
-        log_probs = nn.expand(
+        init_inputs = paddle.full(
+            shape=[self.batch_size, self.beam_size],
+            fill_value=self.start_token_tensor,
+            dtype=self.start_token_tensor.dtype)
+        log_probs = paddle.tile(
             tensor.assign(
                 np.array(
                     [[0.] + [-self.kinf] * (self.beam_size - 1)],
@@ -1213,7 +1212,7 @@ class BeamSearchDecoder(Decoder):
         scores = log_probs
         scores = nn.reshape(scores, [-1, self.beam_size * self.vocab_size])
         # TODO: add grad for topk then this beam search can be used to train
-        topk_scores, topk_indices = nn.topk(input=scores, k=self.beam_size)
+        topk_scores, topk_indices = paddle.topk(x=scores, k=self.beam_size)
         beam_indices = nn.elementwise_floordiv(topk_indices,
                                                self.vocab_size_tensor)
         token_indices = nn.elementwise_mod(topk_indices, self.vocab_size_tensor)