Fix

Author: co63oc

paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h

@@ -485,12 +485,12 @@ void index_put_grad(const Tensor& x,
             indices.cend(),
             [](const Tensor& t) { return has_dynamic_shape(t.shape()); }) ||
         has_dynamic_shape(out_grad.shape())) {
-      for (int i = 0; i < indices.size(); ++i) {
+      for (size_t i = 0; i < indices.size(); ++i) {
         indices_vec.push_back(backend::unsqueeze<T>(
             indices[i], full<T>({1}, -1, DataType::INT64, indices[i].place())));
       }
     } else {
-      for (int i = 0; i < indices.size(); ++i) {
+      for (size_t i = 0; i < indices.size(); ++i) {
         indices_vec.push_back(unsqueeze<T>(indices[i], {-1}));
       }
     }
@@ -617,7 +617,7 @@ void index_add_grad(const Tensor& index,
       auto index_expand_shape = std::vector<int64_t>(out_grad_shape.size(), 1);
       auto index_unsqueeze_shape =
           std::vector<int64_t>(out_grad_shape.size(), 1);
-      for (int i = 0; i < out_grad_shape.size(); ++i) {
+      for (int i = 0; i < static_cast<int>(out_grad_shape.size()); ++i) {
         if (i != axis) {
           index_expand_shape.at(i) = out_grad_shape.at(i);
         } else {

paddle/phi/backends/xpu/xpu_context.cc

@@ -169,7 +169,7 @@ struct XPUContext::Impl {
                         errors::PreconditionNotMet(
                             "No ctx_guard when overload_free is called"));
       allocations_to_free_.pop_back();
-      VLOG(3) << "XHPC ctx_guard destropyed, " << GetStackLevel()
+      VLOG(3) << "XHPC ctx_guard destroyed, " << GetStackLevel()
               << " are in use now.";
     }
 

paddle/phi/kernels/cpu/grid_sample_kernel.cc

@@ -209,7 +209,7 @@ static void BilinearInter(const CPUContext& dev_ctx,
   auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
   auto v_es_t = EigenTensor<T, 4>::From(v_es);
   auto output_t = EigenTensor<T, 4>::From(*out);
-  // bilinear interpolaetion by 4 corner points
+  // bilinear interpolation by 4 corner points
   output_t.device(place) = v_wn_t * d_e_scaled_t * d_s_scaled_t +
                            v_en_t * d_w_scaled_t * d_s_scaled_t +
                            v_ws_t * d_e_scaled_t * d_n_scaled_t +
@@ -292,7 +292,7 @@ static void Bilinear3DInter(const CPUContext& dev_ctx,
   auto v_bws_t = EigenTensor<T, 5>::From(v_bws);
   auto v_bes_t = EigenTensor<T, 5>::From(v_bes);
   auto output_t = EigenTensor<T, 5>::From(*out);
-  // bilinear interpolaetion by 4 corner points
+  // bilinear interpolation by 4 corner points
   output_t.device(place) =
       v_twn_t * d_e_scaled_t * d_s_scaled_t * d_b_scaled_t +
       v_ten_t * d_w_scaled_t * d_s_scaled_t * d_b_scaled_t +

python/paddle/nn/functional/input.py

@@ -202,7 +202,7 @@ def embedding(
 
     Args:
         x(Tensor): A Tensor with type int32/int64, which contains the id information. The value of the input id should
-            satisfy :math:`0<= id < weight.shape[0]` .
+            satisfy :math:`0 <= id < weight.shape[0]` .
         weight (Tensor): The weight. A Tensor with shape of lookup table parameter. It should have two elements which
             indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
         sparse(bool, optional): The flag indicating whether to use sparse update. This parameter only

python/paddle/nn/functional/loss.py

@@ -294,7 +294,7 @@ def base_softmax_with_cross_entropy(
         # label = paddle.unsqueeze(label, axis)
         # ------
 
-        # notice : dim of label [-1,2,10] while logits is [-1,1] ,if use unsquezee
+        # notice : dim of label [-1,2,10] while logits is [-1,1], if use unsqueeze
         # logits [-1,1]->[-1,1,1], but input need 1 != 2 so change
         # the modified function make logits [-1,1] -> [-1,2] (uncertain)
         # another possible modify [-1,1] -> [-1,2,1]
@@ -2971,8 +2971,8 @@ def cross_entropy(
 
     if input_dims - 1 != label_dims and input_dims != label_dims:
         raise ValueError(
-            f'Expected nput_dims - 1 = label_dims or input_dims == label_dims\
-             (got nput_dims{input_dims}, label_dims{label_dims})'
+            f'Expected input_dims - 1 = label_dims or input_dims == label_dims\
+             (got input_dims{input_dims}, label_dims{label_dims})'
         )
 
     if label_smoothing > 0.0:
@@ -3457,7 +3457,7 @@ def multi_label_soft_margin_loss(
     Parameters:
         input (Tensor): Input tensor, the data type is float32 or float64. Shape is (N, C), where C is number of classes, and if shape is more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1.
         label (Tensor): Label tensor, the data type is float32 or float64. The shape of label is the same as the shape of input.
-        weight (Tensor,optional): a manual rescaling weight given to each class.
+        weight (Tensor, optional): a manual rescaling weight given to each class.
                 If given, has to be a Tensor of size C and the data type is float32, float64.
                 Default is ``'None'`` .
         reduction (str, optional): Indicate how to average the loss by batch_size,
@@ -3472,7 +3472,7 @@ def multi_label_soft_margin_loss(
     Shape:
         input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operation operates over all the elements.
         label: N-D Tensor, same shape as the input.
-        weight:N-D Tensor, the shape is [N,1]
+        weight: N-D Tensor, the shape is [N,1]
         output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input.
 
     Returns:
@@ -3513,13 +3513,13 @@ def multi_label_soft_margin_loss(
             input,
             'input',
             ['float32', 'float64'],
-            'multilabel_soft_margin_loss',
+            'multi_label_soft_margin_loss',
         )
         check_variable_and_dtype(
             label,
             'label',
             ['float32', 'float64'],
-            'multilabel_soft_margin_loss',
+            'multi_label_soft_margin_loss',
         )
 
     loss = -(
@@ -3533,7 +3533,7 @@ def multi_label_soft_margin_loss(
                 weight,
                 'weight',
                 ['float32', 'float64'],
-                'multilabel_soft_margin_loss',
+                'multi_label_soft_margin_loss',
             )
         loss = loss * weight
 

test/legacy_test/test_index_fill.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import unittest
 from itertools import combinations
 
 import numpy as np
+from op_test import get_places
 
 import paddle
 from paddle.base import Program
@@ -44,17 +44,9 @@ def setUp(self):
             self.index_type
         )
 
-        self.place = []
-        if (
-            os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
-            in ['1', 'true', 'on']
-            or not paddle.is_compiled_with_cuda()
-        ):
-            self.place.append('cpu')
-        if self.dtype_np == 'float16':
-            self.place = []
-        if paddle.is_compiled_with_cuda():
-            self.place.append('gpu')
+        self.place = get_places(string_format=True)
+        if self.dtype_np == 'float16' and 'cpu' in self.place:
+            self.place.remove('cpu')
 
     def init_setting(self):
         self.dtype_np = 'float64'
@@ -158,17 +150,9 @@ def setUp(self):
             self.index_type
         )
 
-        self.place = []
-        if (
-            os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
-            in ['1', 'true', 'on']
-            or not paddle.is_compiled_with_cuda()
-        ):
-            self.place.append('cpu')
-        if self.dtype_np == 'float16':
-            self.place = []
-        if paddle.is_compiled_with_cuda():
-            self.place.append('gpu')
+        self.place = get_places(string_format=True)
+        if self.dtype_np == 'float16' and 'cpu' in self.place:
+            self.place.remove('cpu')
 
     def init_setting(self):
         self.dtype_np = 'float64'

test/legacy_test/test_multilabelmarginloss.py

@@ -13,10 +13,10 @@
 # limitations under the License.
 
 
-import os
 import unittest
 
 import numpy as np
+from op_test import get_places
 
 import paddle
 
@@ -222,18 +222,8 @@ def test_MultiLabelMarginLoss(self):
             )
             label[i, :num_valid] = valid_labels
 
-        places = []
-        if (
-            os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
-            in ['1', 'true', 'on']
-            or not paddle.device.is_compiled_with_cuda()
-        ):
-            places.append(paddle.CPUPlace())
-        if paddle.device.is_compiled_with_cuda():
-            places.append(paddle.CUDAPlace(0))
-
         reductions = ['sum', 'mean', 'none']
-        for place in places:
+        for place in get_places():
             for reduction in reductions:
                 expected = calc_multi_label_margin_loss(
                     input=input, label=label, reduction=reduction