Fix (#69437)

co63oc · web-flow · commit f8d654173c27 · 2024-11-20T09:35:02.000+08:00
diff --git a/test/cpp/fluid/platform/bfloat16_test.cu b/test/cpp/fluid/platform/bfloat16_test.cu
@@ -80,7 +80,7 @@ TEST(bfloat16, lod_tensor_on_gpu) {
   // GPU DenseTensor to CPU DenseTensor
   framework::TensorCopy(gpu_tensor, CPUPlace(), gpu_ctx, &dst_tensor);
 
-  // Sync before comparing LoDTensors
+  // Sync before comparing DenseTensors
   gpu_ctx.Wait();
   const bfloat16 *dst_ptr = dst_tensor.data<bfloat16>();
   ASSERT_NE(src_ptr, dst_ptr);
diff --git a/test/cpp/fluid/platform/float16_test.cu b/test/cpp/fluid/platform/float16_test.cu
@@ -341,7 +341,7 @@ TEST(float16, lod_tensor_on_gpu) {
   // GPU DenseTensor to CPU DenseTensor
   framework::TensorCopy(gpu_tensor, CPUPlace(), gpu_ctx, &dst_tensor);
 
-  // Sync before comparing LoDTensors
+  // Sync before comparing DenseTensors
   gpu_ctx.Wait();
   const float16 *dst_ptr = dst_tensor.data<float16>();
   ASSERT_NE(src_ptr, dst_ptr);
diff --git a/test/cpp/fluid/save_load_combine_op_test.cc b/test/cpp/fluid/save_load_combine_op_test.cc
@@ -79,7 +79,7 @@ void CheckValues(T* expect,
   }
 }
 
-// Here, we create 4 LoDTensors and use save_combine_op to first save these
+// Here, we create 4 DenseTensors and use save_combine_op to first save these
 // in a single file. Then, we use load_combine_op to load these sequentially
 template <typename T, typename U>
 void SaveLoadCombineOp() {
diff --git a/test/deprecated/book/test_word2vec_book_deprecated.py b/test/deprecated/book/test_word2vec_book_deprecated.py
@@ -217,10 +217,10 @@ def infer(target, save_dirname=None):
         word_dict = paddle.dataset.imikolov.build_dict()
         dict_size = len(word_dict)
 
-        # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word
+        # Setup inputs by creating 4 DenseTensors representing 4 words. Here each word
         # is simply an index to look up for the corresponding word vector and hence
         # the shape of word (base_shape) should be [1]. The recursive_sequence_lengths,
-        # which is length-based level of detail (lod) of each LoDTensor, should be [[1]]
+        # which is length-based level of detail (lod) of each DenseTensor, should be [[1]]
         # meaning there is only one level of detail and there is only one sequence of
         # one word on this level.
         # Note that recursive_sequence_lengths should be a list of lists.
diff --git a/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py b/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py
@@ -61,7 +61,7 @@ def multiclass_nms(
                            N is the batch size. Each bounding box has four
                            coordinate values and the layout is
                            [xmin, ymin, xmax, ymax], when box size equals to 4.
-                           2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
+                           2. (DenseTensor) A 3-D Tensor with shape [M, C, 4]
                            M is the number of bounding boxes, C is the
                            class number
         scores (Tensor): Two types of scores are supported:
@@ -72,7 +72,7 @@ class number
                            are total M scores which corresponding M bounding
                            boxes. Please note, M is equal to the 2nd dimension
                            of BBoxes.
-                           2. (LoDTensor) A 2-D DenseTensor with shape [M, C].
+                           2. (DenseTensor) A 2-D DenseTensor with shape [M, C].
                            M is the number of bbox, C is the class number.
                            In this case, input BBoxes should be the second
                            case with shape [M, C, 4].
diff --git a/test/deprecated/legacy_test/test_eager_run_program_deprecated.py b/test/deprecated/legacy_test/test_eager_run_program_deprecated.py
@@ -39,8 +39,8 @@ def _append_backward_desc(main_program, outs):
 
 # def _set_grad_type(params, train_program):
 #     # NOTE: if user set sparse gradient mode, the param's gradient
-#     # will be SelectedRows, not LoDTensor. But tracer will just
-#     # set param grad Tensor by forward Tensor(LoDTensor)
+#     # will be SelectedRows, not DenseTensor. But tracer will just
+#     # set param grad Tensor by forward Tensor(DenseTensor)
 #     # If we don't change grad_var type here, RunProgramOp need
 #     # transform SelectedRows to DenseTensor forcibly, it may not
 #     # be user wanted result.
diff --git a/test/deprecated/legacy_test/test_run_program_op_deprecated.py b/test/deprecated/legacy_test/test_run_program_op_deprecated.py
@@ -64,7 +64,7 @@ def _build_program_by_desc(program_desc):
 
 # NOTE: Because RunProgramOp has a special output of type std::vector<Scope *>,
 # the OpTest cannot be used in RunProgramOp. The variable type cannot be specified
-# when creating output variables in OpTest, default type is LoDTensor
+# when creating output variables in OpTest, default type is DenseTensor
 # NOTE: the gradient test method in OpTest also cannot be used for RunProgramOp,
 # because it hold BlockDesc type attr, OperatorFactory can't parse this attr type
 # when create Operator, so here compare gradients with static graph
diff --git a/test/ir/test_ir_fusion_group_pass.py b/test/ir/test_ir_fusion_group_pass.py
@@ -62,7 +62,9 @@ def _feed_random_data(self, feed_vars):
         feeds = {}
         for var in feed_vars:
             if var.type != base.core.VarDesc.VarType.DENSE_TENSOR:
-                raise TypeError("Feed data of non LoDTensor is not supported.")
+                raise TypeError(
+                    "Feed data of non DenseTensor is not supported."
+                )
 
             shape = var.shape
             if var.dtype == paddle.float32:
diff --git a/test/legacy_test/nets.py b/test/legacy_test/nets.py
@@ -284,14 +284,14 @@ def sequence_conv_pool(
     """
         :api_attr: Static Graph
 
-    **This api takes input as an LoDTensor. If input is a Tensor, please use**
+    **This api takes input as an DenseTensor. If input is a Tensor, please use**
     :ref:`api_base_nets_simple_img_conv_pool` **instead**
 
     The sequence_conv_pool is composed of :ref:`api_base_layers_sequence_conv`
     and :ref:`api_base_layers_sequence_pool` .
 
     Args:
-        input (Tensor): 2-D LoDTensor, the input of sequence_conv,
+        input (Tensor): 2-D DenseTensor, the input of sequence_conv,
             which supports variable-time length input sequence.
             The underlying of input is a matrix with shape
             (T, N), where T is the total time steps in this mini-batch and N is
@@ -370,7 +370,7 @@ def glu(input, dim=-1):
     <https://arxiv.org/pdf/1612.08083.pdf>`_.
 
     Args:
-        input (Variable): The input variable which is a Tensor or LoDTensor.
+        input (Variable): The input variable which is a Tensor.
                           The supported data types include float32, float64
                           and float16 (only for GPU).
         dim (int, optional): The dimension along which to split. If :math:`dim < 0`, the
diff --git a/test/legacy_test/test_paddle_save_load.py b/test/legacy_test/test_paddle_save_load.py
@@ -430,7 +430,7 @@ def test_single_pickle_var_static(self):
         # set_tensor(np.ndarray)
         set_value(var, np_static, scope)
         np.testing.assert_array_equal(origin_tensor, np.array(tensor))
-        # set_tensor(LoDTensor)
+        # set_tensor(DenseTensor)
         self.set_zero(prog, place, scope)
         set_value(var, lod_static, scope)
         np.testing.assert_array_equal(origin_tensor, np.array(tensor))

Original file line number	Diff line number	Diff line change
`@@ -79,7 +79,7 @@ void CheckValues(T* expect,`
`79`	`79`	`}`
`80`	`80`	`}`
`81`	`81`
`82`		`-// Here, we create 4 LoDTensors and use save_combine_op to first save these`
	`82`	`+// Here, we create 4 DenseTensors and use save_combine_op to first save these`
`83`	`83`	`// in a single file. Then, we use load_combine_op to load these sequentially`
`84`	`84`	`template <typename T, typename U>`
`85`	`85`	`void SaveLoadCombineOp() {`