use tensor.shape bug not paddle.shape(tensor) (#8260)

* use tensor.shape bug not paddle.shape(tensor)

* refine

* refine

examples/language_model/moe/dygraph/modeling.py

@@ -748,8 +748,8 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if position_ids is None:
             past_length = 0
             if cache is not None:
-                past_length = paddle.shape(cache[0].k)[-2]
-            position_ids = paddle.arange(past_length, paddle.shape(input_ids)[-1] + past_length, dtype="int64")
+                past_length = cache[0].k.shape[-2]
+            position_ids = paddle.arange(past_length, input_ids.shape[-1] + past_length, dtype="int64")
             position_ids = position_ids.unsqueeze(0)
             # .expand_as(input_ids)
             position_ids = paddle.expand_as(position_ids, input_ids)

examples/model_interpretation/task/senti/rnn/model.py

@@ -207,7 +207,7 @@ def forward(self, input, mask=None):
         # Shape: (batch_size, max_seq_len, hidden_size)
         h = paddle.add_n([forward_input, backward_input])
         # Shape: (batch_size, hidden_size, 1)
-        att_weight = self.att_weight.tile(repeat_times=(paddle.shape(h)[0], 1, 1))
+        att_weight = self.att_weight.tile(repeat_times=(h.shape[0], 1, 1))
         # Shape: (batch_size, max_seq_len, 1)
         att_score = paddle.bmm(paddle.tanh(h), att_weight)
         if mask is not None:
@@ -246,20 +246,18 @@ def forward(self, input, mask=None):
                 Tensor is a bool tensor, whose each element identifies whether the input word id is pad token or not.
                 Defaults to `None
         """
-        weight = self.input_weight.tile(
-            repeat_times=(paddle.shape(input)[0], 1, 1)
-        )  # tensor[batch, hidden_size, hidden_size]
-        bias = self.bias.tile(repeat_times=(paddle.shape(input)[0], 1, 1))  # tensor[batch, 1, hidden_size]
+        weight = self.input_weight.tile(repeat_times=(input.shape[0], 1, 1))  # tensor[batch, hidden_size, hidden_size]
+        bias = self.bias.tile(repeat_times=(input.shape[0], 1, 1))  # tensor[batch, 1, hidden_size]
         word_squish = paddle.bmm(input, weight) + bias  # Shape: (batch_size, seq_len, hidden_size)
         att_context_vector = self.att_context_vector.tile(
-            repeat_times=(paddle.shape(input)[0], 1, 1)
+            repeat_times=(input.shape[0], 1, 1)
         )  # Shape: (batch_size, hidden_size, 1)
         att_score = paddle.bmm(word_squish, att_context_vector)  # tensor[batch_size, seq_len, 1]
         if mask is not None:
             # mask, remove the effect of 'PAD'
             mask = paddle.cast(mask, dtype="float32")
             mask = mask.unsqueeze(axis=-1)
-            inf_tensor = paddle.full(shape=paddle.shape(mask), dtype="float32", fill_value=-INF)
+            inf_tensor = paddle.full(shape=mask.shape, dtype="float32", fill_value=-INF)
             att_score = paddle.multiply(att_score, mask) + paddle.multiply(inf_tensor, (1 - mask))
         att_weight = F.softmax(att_score, axis=1)  # tensor[batch_size, seq_len, 1]
 

examples/simultaneous_translation/stacl/demo/model_demo.py

@@ -34,7 +34,7 @@ def greedy_search(self, src_word, max_len=256, waitk=-1, caches=None, bos_id=Non
         So, it needsprevious state(caches) and last one of generated
         tokens id last time.
         """
-        src_max_len = paddle.shape(src_word)[-1]
+        src_max_len = src_word.shape[-1]
         base_attn_bias = (
             paddle.cast(src_word == self.bos_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
         )

examples/simultaneous_translation/stacl/model.py

@@ -15,11 +15,11 @@
 from __future__ import print_function
 
 import numpy as np
-
 import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
-from paddlenlp.transformers import WordEmbedding, PositionalEmbedding
+
+from paddlenlp.transformers import PositionalEmbedding, WordEmbedding
 
 
 class CrossEntropyCriterion(nn.Layer):
@@ -190,8 +190,8 @@ def __init__(
             self.linear = nn.Linear(in_features=d_model, out_features=trg_vocab_size, bias_attr=False)
 
     def forward(self, src_word, trg_word):
-        src_max_len = paddle.shape(src_word)[-1]
-        trg_max_len = paddle.shape(trg_word)[-1]
+        src_max_len = src_word.shape[-1]
+        trg_max_len = trg_word.shape[-1]
         base_attn_bias = (
             paddle.cast(src_word == self.bos_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
         )
@@ -236,7 +236,7 @@ def beam_search(self, src_word, beam_size=4, max_len=256, waitk=-1):
         raise NotImplementedError
 
     def greedy_search(self, src_word, max_len=256, waitk=-1):
-        src_max_len = paddle.shape(src_word)[-1]
+        src_max_len = src_word.shape[-1]
         base_attn_bias = (
             paddle.cast(src_word == self.bos_id, dtype=paddle.get_default_dtype()).unsqueeze([1, 2]) * -1e9
         )

examples/text_classification/rnn/model.py

@@ -253,7 +253,7 @@ def forward(self, input, mask=None):
         # Shape: (batch_size, max_seq_len, hidden_size)
         h = paddle.add_n([forward_input, backward_input])
         # Shape: (batch_size, hidden_size, 1)
-        att_weight = self.att_weight.tile(repeat_times=(paddle.shape(h)[0], 1, 1))
+        att_weight = self.att_weight.tile(repeat_times=(h.shape[0], 1, 1))
         # Shape: (batch_size, max_seq_len, 1)
         att_score = paddle.bmm(paddle.tanh(h), att_weight)
         if mask is not None:
@@ -292,19 +292,19 @@ def forward(self, input, mask=None):
                 Tensor is a bool tensor, whose each element identifies whether the input word id is pad token or not.
                 Defaults to `None
         """
-        weight = self.input_weight.tile(repeat_times=(paddle.shape(input)[0], 1, 1))
-        bias = self.bias.tile(repeat_times=(paddle.shape(input)[0], 1, 1))
+        weight = self.input_weight.tile(repeat_times=(input.shape[0], 1, 1))
+        bias = self.bias.tile(repeat_times=(input.shape[0], 1, 1))
         # Shape: (batch_size, max_seq_len, hidden_size)
         word_squish = paddle.bmm(input, weight) + bias
 
-        att_context_vector = self.att_context_vector.tile(repeat_times=(paddle.shape(input)[0], 1, 1))
+        att_context_vector = self.att_context_vector.tile(repeat_times=(input.shape[0], 1, 1))
         # Shape: (batch_size, max_seq_len, 1)
         att_score = paddle.bmm(word_squish, att_context_vector)
         if mask is not None:
             # mask, remove the effect of 'PAD'
             mask = paddle.cast(mask, dtype="float32")
             mask = mask.unsqueeze(axis=-1)
-            inf_tensor = paddle.full(shape=paddle.shape(mask), dtype="float32", fill_value=-INF)
+            inf_tensor = paddle.full(shape=mask.shape, dtype="float32", fill_value=-INF)
             att_score = paddle.multiply(att_score, mask) + paddle.multiply(inf_tensor, (1 - mask))
         att_weight = F.softmax(att_score, axis=1)
 

examples/text_to_sql/RAT-SQL/text2sql/utils/nn_utils.py

@@ -74,7 +74,7 @@ def batch_gather_2d(var, indices):
             "shape of indices error. it should be a 2-D layers. " "but got shape = %s" % (str(indices.shape),)
         )
 
-    batch_size = paddle.shape(indices)[0]
+    batch_size = indices.shape[0]
 
     zero = paddle.to_tensor([0], dtype="int64")
     one = paddle.to_tensor([1], dtype="int64")

llm/ernie-3.5-se/modeling.py

@@ -142,7 +142,7 @@ def scaled_dot_product_attention(
     query_states, key_states, value_states, attention_mask, output_attentions, config, is_causal=True
 ):
 
-    bsz, q_len, num_heads, _ = paddle.shape(query_states)
+    bsz, q_len, num_heads, _ = query_states.shape
     head_dim = config.hidden_size // config.num_attention_heads
     _, kv_seq_len, _, _ = value_states.shape
 
@@ -1054,7 +1054,7 @@ def forward(
         seq_length_with_past = seq_length
         cache_length = 0
         if past_key_values[0] is not None:
-            cache_length = paddle.shape(past_key_values[0][0])[1]
+            cache_length = past_key_values[0][0].shape[1]
             seq_length_with_past += cache_length
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids).astype(self.embed_tokens.weight.dtype)

model_zoo/gpt-3/ppfleetx/models/language_model/gpt/auto/auto_model.py

@@ -735,8 +735,8 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if position_ids is None:
             past_length = 0
             if cache is not None:
-                past_length = paddle.shape(attention_mask)[-1] - 1
-            position_ids = paddle.arange(past_length, paddle.shape(input_ids)[-1] + past_length, dtype=input_ids.dtype)
+                past_length = attention_mask.shape[-1] - 1
+            position_ids = paddle.arange(past_length, input_ids.shape[-1] + past_length, dtype=input_ids.dtype)
             position_ids = position_ids.unsqueeze(0)
             position_ids = paddle.expand_as(position_ids, input_ids)
 
@@ -753,7 +753,7 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if not self.fused_softmax_with_triangular or not paddle.is_compiled_with_cuda():
             # TODO, use registered buffer
             causal_mask = paddle.tensor.triu(
-                paddle.ones((paddle.shape(input_ids)[-1], paddle.shape(input_ids)[-1])) * -1e4, diagonal=1
+                paddle.ones((input_ids.shape[-1], input_ids.shape[-1])) * -1e4, diagonal=1
             )
             if attention_mask is not None:
                 if len(attention_mask.shape) == 2:
@@ -972,7 +972,7 @@ def get_logits_processor(
 
     def expand_inputs_for_generation(self, input_ids, expand_size, attention_mask=None, **model_kwargs):
 
-        index = paddle.tile(paddle.arange(paddle.shape(input_ids)[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
+        index = paddle.tile(paddle.arange(input_ids.shape[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
 
         input_ids = paddle.gather(input_ids, index)
 
@@ -1109,11 +1109,11 @@ def TopPProcess(probs, top_p, min_tokens_to_keep):
             probs = paddle.where(condition, paddle.full_like(probs, 0.0), probs)
             return probs
 
-        batch_size, cur_len = paddle.shape(input_ids)
+        batch_size, cur_len = input_ids.shape
         # used for compute on gpu, avoid memcpy D2H
         cur_len_gpu = paddle.full([1], cur_len, dtype="int64")
 
-        origin_len = paddle.shape(input_ids)[1]
+        origin_len = input_ids.shape[1]
         # used for compute on gpu, avoid memcpy D2H
         origin_len_gpu = paddle.full([1], origin_len, dtype="int64")
 
@@ -1167,7 +1167,7 @@ def _post_process_(outputs, input_ids, cur_len, origin_len, scores, unfinished_f
                         raise ImportError(
                             "please install ppfleetx_ops by 'cd ppfleetx/ops && python setup_cuda.py install'!"
                         )
-                    top_ps_tensor = paddle.full(shape=[paddle.shape(probs)[0]], fill_value=top_p, dtype=probs.dtype)
+                    top_ps_tensor = paddle.full(shape=[probs.shape[0]], fill_value=top_p, dtype=probs.dtype)
                     # TODO fake random seed here
                     # Users should set the random seed dynamically when inference
                     _, next_tokens = topp_sampling(probs, top_ps_tensor, random_seed=100)
@@ -1299,7 +1299,7 @@ def forward(self, input_ids=None, **model_kwargs):
 
         if model_kwargs.get("position_ids", None) is None:
             model_kwargs["position_ids"] = paddle.arange(
-                0, paddle.shape(model_kwargs["attention_mask"])[-1], dtype=input_ids.dtype
+                0, model_kwargs["attention_mask"].shape[-1], dtype=input_ids.dtype
             ).unsqueeze(0)
 
         self.is_encoder_decoder = False

model_zoo/gpt-3/ppfleetx/models/language_model/gpt/dygraph/hybrid_model.py

@@ -837,8 +837,8 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if position_ids is None:
             past_length = 0
             if cache is not None:
-                past_length = paddle.shape(attention_mask)[-1] - 1
-            position_ids = paddle.arange(past_length, paddle.shape(input_ids)[-1] + past_length, dtype=input_ids.dtype)
+                past_length = attention_mask.shape[-1] - 1
+            position_ids = paddle.arange(past_length, input_ids.shape[-1] + past_length, dtype=input_ids.dtype)
             position_ids = position_ids.unsqueeze(0)
             # .expand_as(input_ids)
             position_ids = paddle.expand_as(position_ids, input_ids)
@@ -851,7 +851,7 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if not self.fused_softmax_with_triangular or not paddle.is_compiled_with_cuda():
             # TODO, use registered buffer
             causal_mask = paddle.tensor.triu(
-                paddle.ones((paddle.shape(input_ids)[-1], paddle.shape(input_ids)[-1])) * -1e4, diagonal=1
+                paddle.ones((input_ids.shape[-1], input_ids.shape[-1])) * -1e4, diagonal=1
             )
             if attention_mask is not None:
                 if len(attention_mask.shape) == 2:
@@ -1304,7 +1304,7 @@ def get_logits_processor(
 
     def expand_inputs_for_generation(self, input_ids, expand_size, attention_mask=None, **model_kwargs):
 
-        index = paddle.tile(paddle.arange(paddle.shape(input_ids)[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
+        index = paddle.tile(paddle.arange(input_ids.shape[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
 
         input_ids = paddle.gather(input_ids, index)
 

model_zoo/gpt-3/ppfleetx/models/language_model/gpt/dygraph/single_model.py

@@ -602,8 +602,8 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if position_ids is None:
             past_length = 0
             if cache is not None:
-                past_length = paddle.shape(attention_mask)[-1] - 1
-            position_ids = paddle.arange(past_length, paddle.shape(input_ids)[-1] + past_length, dtype=input_ids.dtype)
+                past_length = attention_mask.shape[-1] - 1
+            position_ids = paddle.arange(past_length, input_ids.shape[-1] + past_length, dtype=input_ids.dtype)
             position_ids = position_ids.unsqueeze(0)
             # .expand_as(input_ids)
             position_ids = paddle.expand_as(position_ids, input_ids)
@@ -615,7 +615,7 @@ def forward(self, input_ids, position_ids=None, attention_mask=None, use_cache=F
         if not self.fused_softmax_with_triangular or not paddle.is_compiled_with_cuda():
             # TODO, use registered buffer
             causal_mask = paddle.tensor.triu(
-                paddle.ones((paddle.shape(input_ids)[-1], paddle.shape(input_ids)[-1])) * -1e4, diagonal=1
+                paddle.ones((input_ids.shape[-1], input_ids.shape[-1])) * -1e4, diagonal=1
             )
             if attention_mask is not None:
                 if len(attention_mask.shape) == 2:
@@ -848,7 +848,7 @@ def get_logits_processor(
 
     def expand_inputs_for_generation(self, input_ids, expand_size, attention_mask=None, **model_kwargs):
 
-        index = paddle.tile(paddle.arange(paddle.shape(input_ids)[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
+        index = paddle.tile(paddle.arange(input_ids.shape[0]).unsqueeze(-1), [1, expand_size]).reshape([-1])
 
         input_ids = paddle.gather(input_ids, index)
 
@@ -1039,7 +1039,7 @@ def _post_process_(outputs, input_ids, cur_len, origin_len, scores, unfinished_f
                         raise ImportError(
                             "please install ppfleetx_ops by 'cd ppfleetx/ops && python setup_cuda.py install'!"
                         )
-                    top_ps_tensor = paddle.full(shape=[paddle.shape(probs)[0]], fill_value=top_p, dtype=probs.dtype)
+                    top_ps_tensor = paddle.full(shape=[probs.shape[0]], fill_value=top_p, dtype=probs.dtype)
                     _, next_tokens = topp_sampling(probs, top_ps_tensor, random_seed=100)
                 else:
                     probs = TopPProcess(probs, top_p, min_tokens_to_keep)
@@ -1194,7 +1194,7 @@ def forward(self, input_ids=None, **model_kwargs):
 
         if model_kwargs.get("position_ids", None) is None:
             model_kwargs["position_ids"] = paddle.arange(
-                0, paddle.shape(model_kwargs["attention_mask"])[-1], dtype=input_ids.dtype
+                0, model_kwargs["attention_mask"].shape[-1], dtype=input_ids.dtype
             ).unsqueeze(0)
 
         self.is_encoder_decoder = False

paddlenlp/generation/utils.py

@@ -412,9 +412,9 @@ def get_logits_processor(
     @staticmethod
     def expand_inputs_for_generation(input_ids, expand_size, attention_mask=None, **model_kwargs):
 
-        index = paddle.tile(
-            paddle.arange(paddle.shape(input_ids)[0], dtype="int64").unsqueeze(-1), [1, expand_size]
-        ).reshape([-1])
+        index = paddle.tile(paddle.arange(input_ids.shape[0], dtype="int64").unsqueeze(-1), [1, expand_size]).reshape(
+            [-1]
+        )
 
         input_ids = paddle.gather(input_ids, index)
 
@@ -1340,11 +1340,11 @@ def sample_d2s(
                 "you should not specify InputSpec for top_k and top_p parameters, one of InputSpec is expected"
             )
 
-        batch_size, cur_len = paddle.shape(input_ids)
+        batch_size, cur_len = input_ids.shape
         # used for compute on gpu, avoid memcpy D2H
         cur_len_gpu = paddle.full([1], cur_len, dtype="int64")
 
-        origin_len = paddle.shape(input_ids)[1]
+        origin_len = input_ids.shape[1]
         # used for compute on gpu, avoid memcpy D2H
         origin_len_gpu = paddle.full([1], origin_len, dtype="int64")
 
@@ -1384,7 +1384,7 @@ def _post_process_(outputs, input_ids, cur_len, origin_len, scores, unfinished_f
             # compute next_tokens
             if use_top_p:
                 logits = logits / temperature
-                top_ps_tensor = paddle.full(shape=[paddle.shape(probs)[0], 1], fill_value=top_p, dtype=probs.dtype)
+                top_ps_tensor = paddle.full(shape=[probs.shape[0], 1], fill_value=top_p, dtype=probs.dtype)
                 _, next_tokens = paddle.tensor.top_p_sampling(probs, top_ps_tensor)
             else:
                 probs = TopKProcess(probs, top_k, min_tokens_to_keep)
@@ -1428,7 +1428,7 @@ def _post_process_(outputs, input_ids, cur_len, origin_len, scores, unfinished_f
 
         attn_mask = model_kwargs["attention_mask"]
         # make the shape of attention_mask = (-1, -1, -1, -1) in dy2static.
-        model_kwargs["attention_mask"] = paddle.reshape(attn_mask, paddle.shape(attn_mask))
+        model_kwargs["attention_mask"] = paddle.reshape(attn_mask, attn_mask.shape)
         model_kwargs["cache"] = outputs[1] if isinstance(outputs, tuple) else None
         max_new_tokens = paddle.full([1], max_new_tokens + cur_len - 1, dtype="int64")
 

paddlenlp/layers/crf.py

@@ -303,7 +303,7 @@ def __init__(self, transitions, with_start_stop_tag=True):
         if with_start_stop_tag:
             self.start_idx = -1
             self.stop_idx = -2
-        self.num_tags = paddle.shape(transitions)[0]
+        self.num_tags = transitions.shape[0]
 
         self._initial_alpha = None
         self._index = None
@@ -312,7 +312,7 @@ def __init__(self, transitions, with_start_stop_tag=True):
 
     def _initialize_alpha(self, batch_size):
         # alpha accumulate the path value to get the different next tag
-        if self._initial_alpha is None or batch_size > paddle.shape(self._initial_alpha)[0]:
+        if self._initial_alpha is None or batch_size > self._initial_alpha.shape[0]:
             # Initialized by a small value.
             initial_alpha = paddle.full([batch_size, self.num_tags - 1], dtype="float32", fill_value=-10000.0)
             # alpha_start fill_value = 0. > -10000., means the first one step START gets the most score.
@@ -336,7 +336,7 @@ def forward(self, inputs, lengths):
             The `paths` tensor containing the highest scoring tag indices.
             Its dtype is int64 and has a shape of `[batch_size, sequence_length]`.
         """
-        input_shape = paddle.shape(inputs)
+        input_shape = inputs.shape
         batch_size = input_shape[0]
         n_label = input_shape[2]
 
@@ -412,6 +412,6 @@ def forward(self, inputs, lengths):
         return scores, batch_path
 
     def _get_batch_index(self, batch_size):
-        if self._batch_index is None or batch_size != paddle.shape(self._batch_index)[0]:
+        if self._batch_index is None or batch_size != self._batch_index.shape[0]:
             self._batch_index = paddle.arange(end=batch_size, dtype="int64")
         return self._batch_index