【Hackathon 7th】fix whisper at Paddle 3.0 (#3880)

* fix whisper at Paddle 3.0

* fix whisper at Paddle 3.0

* fix whisper at Paddle 3.0

* fix lint

* fix

* fix whisper ci

* Update TTSCppFrontend

* Update utils

* Update steps

* Update utils

* Update __init__.py

* Update whisper.py

* Update utils

* Update utils

paddlespeech/s2t/models/whisper/whisper.py

@@ -109,11 +109,11 @@ class MultiHeadAttention(nn.Layer):
         n_batch, n_ctx, n_state = q.shape
         scale = (n_state // self.n_head)**-0.25
         q = paddle.transpose(
-            q.view(*q.shape[:2], self.n_head, -1), (0, 2, 1, 3)) * scale
+            q.reshape([*q.shape[:2], self.n_head, -1]), (0, 2, 1, 3)) * scale
         k = paddle.transpose(
-            k.view(*k.shape[:2], self.n_head, -1), (0, 2, 3, 1)) * scale
+            k.reshape([*k.shape[:2], self.n_head, -1]), (0, 2, 3, 1)) * scale
         v = paddle.transpose(
-            v.view(*v.shape[:2], self.n_head, -1), (0, 2, 1, 3))
+            v.reshape([*v.shape[:2], self.n_head, -1]), (0, 2, 1, 3))
 
         qk = q @ k
         if mask is not None:
@@ -823,7 +823,7 @@ class BeamSearchDecoder(TokenDecoder):
         if self.finished_sequences is None:  # for the first update
             self.finished_sequences = [{} for _ in range(batch_size)]
 
-        logprobs = F.log_softmax(logits, axis=-1, dtype=paddle.float32)
+        logprobs = F.log_softmax(logits, axis=-1, dtype='float32')
         next_tokens, source_indices, finished_sequences = [], [], []
         for i in range(batch_size):
             scores, sources, finished = {}, {}, {}
@@ -969,7 +969,7 @@ class ApplyTimestampRules(LogitFilter):
             logits[:, last_allowed + 1:] = -np.inf
 
         # if sum of probability over timestamps is above any other token, sample timestamp
-        logprobs = F.log_softmax(logits, axis=-1, dtype=paddle.float32)
+        logprobs = F.log_softmax(logits, axis=-1, dtype='float32')
         for k in range(tokens.shape[0]):
             # When using paddle.logsumexp on a 32GB Tesla-V100 GPU, we encountered CUDA error 700. 
             # To bypass this issue in CI, we have decomposed the operation into separate steps.