Fix dimention dimension (#61247)

co63oc · web-flow · commit 170e8caa07ec · 2024-01-30T10:52:09.000+08:00
diff --git a/paddle/phi/infermeta/binary.cc b/paddle/phi/infermeta/binary.cc
@@ -381,7 +381,7 @@ void CholeskySolveInferMeta(const MetaTensor& x,
       x_dims[x_dims_n - 2],
       y_dims[y_dims_n - 2],
       phi::errors::InvalidArgument("the first dim of Matrix X must be equal to "
-                                   "the fisrt dim of Matrix Y,"
+                                   "the first dim of Matrix Y,"
                                    "But Got %ld and %ld",
                                    x_dims[x_dims_n - 2],
                                    y_dims[y_dims_n - 2]));
@@ -564,7 +564,7 @@ void ConvInferMeta(const MetaTensor& input,
           "length must be equal to 2 for Op(Conv). "
           "But received: input's dimension is %d, input's shape is [%s]; "
           "Attr(stride)'s length is %d, Attr(stride) is [%s]; "
-          "difference of input's dimention and Attr(strides)'s length = %u.",
+          "difference of input's dimension and Attr(strides)'s length = %u.",
           in_dims.size(),
           in_dims,
           strides.size(),
@@ -1540,7 +1540,7 @@ void FusedMatmulInferMeta(const MetaTensor& x,
                           bool transpose_y,
                           const float matmul_alpha,
                           const std::string& fuse_activation,
-                          const float fuse_lapha,
+                          const float fuse_alpha,
                           const float fuse_beat,
                           const float fused_output_scale,
                           const std::vector<int>& fused_reshape_X,
@@ -2279,12 +2279,12 @@ void MatmulInferMeta(const MetaTensor& x,
                     0UL,
                     phi::errors::InvalidArgument(
                         "The Input(x) dims size must be greater than 0,"
-                        " but reviced dims size is 0. "));
+                        " but received dims size is 0. "));
   PADDLE_ENFORCE_GT(ndims_y,
                     0UL,
                     phi::errors::InvalidArgument(
                         "The Input(y) dims size must be greater than 0,"
-                        " but reviced dims size is 0. "));
+                        " but received dims size is 0. "));
 
   bool x_broadcasted = false, y_broadcasted = false;
   if (ndims_x == 1) {
@@ -2463,7 +2463,7 @@ void MatrixNMSInferMeta(const MetaTensor& bboxes,
             "The 2nd dimension of Input(BBoxes) must be equal to "
             "last dimension of Input(Scores), which represents the "
             "predicted bboxes."
-            "But received box_dims[1](%s) != socre_dims[2](%s)",
+            "But received box_dims[1](%s) != score_dims[2](%s)",
             box_dims[1],
             score_dims[2]));
   }
@@ -2688,24 +2688,24 @@ void ApplyPerChannelScaleInferMeta(const MetaTensor& x,
   out->set_layout(x.layout());
 }
 
-inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
+inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratio,
                                bool flip,
-                               std::vector<float>* output_aspect_ratior) {
+                               std::vector<float>* output_aspect_ratio) {
   constexpr float epsilon = 1e-6;
-  output_aspect_ratior->clear();
-  output_aspect_ratior->push_back(1.0f);
-  for (auto ar : input_aspect_ratior) {
+  output_aspect_ratio->clear();
+  output_aspect_ratio->push_back(1.0f);
+  for (auto ar : input_aspect_ratio) {
     bool already_exist = false;
-    for (auto item : *output_aspect_ratior) {
+    for (auto item : *output_aspect_ratio) {
       if (fabs(ar - item) < epsilon) {
         already_exist = true;
         break;
       }
     }
     if (!already_exist) {
-      output_aspect_ratior->push_back(ar);
+      output_aspect_ratio->push_back(ar);
       if (flip) {
-        output_aspect_ratior->push_back(1.0f / ar);
+        output_aspect_ratio->push_back(1.0f / ar);
       }
     }
   }
@@ -3112,21 +3112,21 @@ void LstsqInferMeta(const MetaTensor& x,
       x_rank,
       2,
       phi::errors::InvalidArgument("Expects input tensor x to be not less than "
-                                   "2 dimentions, but got dimention %d",
+                                   "2 dimensions, but got dimension %d",
                                    x_rank));
   PADDLE_ENFORCE_GE(
       y_rank,
       2,
       phi::errors::InvalidArgument("Expects input tensor y to be not less than "
-                                   "2 dimentions, but got dimention %d",
+                                   "2 dimensions, but got dimension %d",
                                    y_rank));
 
   PADDLE_ENFORCE_EQ(
       x_rank,
       y_rank,
       phi::errors::InvalidArgument(
           "Expects input tensor x and y to have the same dimension "
-          "but got x's dimention [%d] and y's dimention [%d]",
+          "but got x's dimension [%d] and y's dimension [%d]",
           x_rank,
           y_rank));
 
@@ -3136,8 +3136,8 @@ void LstsqInferMeta(const MetaTensor& x,
                       y_dims[i],
                       phi::errors::InvalidArgument(
                           "Expects input tensor x and y to have the same batch "
-                          "dimension, but got x's batch dimention [%d] and "
-                          "y's batch dimention [%d] in %d-th dim",
+                          "dimension, but got x's batch dimension [%d] and "
+                          "y's batch dimension [%d] in %d-th dim",
                           x_dims[i],
                           y_dims[i],
                           i));
@@ -3150,7 +3150,7 @@ void LstsqInferMeta(const MetaTensor& x,
       phi::errors::InvalidArgument(
           "Expects input tensor x and y to have the same row dimension "
           "of the inner-most 2-dims matrix, "
-          "but got x's row dimention [%d] and y's row dimention [%d]",
+          "but got x's row dimension [%d] and y's row dimension [%d]",
           m,
           y_dims[y_rank - 2]));
 
diff --git a/paddle/phi/infermeta/binary.h b/paddle/phi/infermeta/binary.h
@@ -282,7 +282,7 @@ void FusedMatmulInferMeta(const MetaTensor& x,
                           bool transpose_y,
                           const float matmul_alpha,
                           const std::string& fuse_activation,
-                          const float fuse_lapha,
+                          const float fuse_alpha,
                           const float fuse_beat,
                           const float fused_output_scale,
                           const std::vector<int>& fused_reshape_X,
diff --git a/paddle/phi/infermeta/fusion.cc b/paddle/phi/infermeta/fusion.cc
@@ -393,7 +393,7 @@ void Conv2dXPUInferMeta(const MetaTensor& x,
           "length must be equal to 2 for Op(Conv_xpu). "
           "But received: input's dimension is %d, input's shape is [%s]; "
           "Attr(stride)'s length is %d, Attr(stride) is [%s]; "
-          "difference of input's dimention and Attr(strides)'s length = %u.",
+          "difference of input's dimension and Attr(strides)'s length = %u.",
           in_dims.size(),
           in_dims,
           strides.size(),
@@ -437,7 +437,7 @@ void Conv2dXPUInferMeta(const MetaTensor& x,
           filter_dims,
           groups));
 
-  // update paddings and dilations accoring to padding_algorithm
+  // update paddings and dilations according to padding_algorithm
   std::vector<int> paddings_vec = paddings;
   std::vector<int> dilations_vec = dilations;
   DDim in_data_dims = common::slice_ddim(in_dims, 2, in_dims.size());
@@ -2316,7 +2316,7 @@ void FusedScaleBiasReluConvBnInferMeta(const MetaTensor& x,
       phi::errors::InvalidArgument(
           "Operator(FusedScaleBiasReluConvBn) only supports data format "
           "of "
-          "channel last (NHWC) now. But recieved: data_format = '%s'.",
+          "channel last (NHWC) now. But received: data_format = '%s'.",
           data_format));
 
   PADDLE_ENFORCE_EQ(
@@ -2352,7 +2352,7 @@ void FusedScaleBiasReluConvBnInferMeta(const MetaTensor& x,
           filter_dims,
           groups));
 
-  // update paddings and dilations accoring to padding_algorithm
+  // update paddings and dilations according to padding_algorithm
   std::vector<int> paddings_vec = paddings;
   std::vector<int> dilations_vec = dilations;
   // get "HW" from "NHWC"
@@ -2458,7 +2458,7 @@ void FusedDconvDreluDbnInferMeta(const MetaTensor& grad_output,
       phi::errors::InvalidArgument(
           "Operator(FusedScaleBiasReluConvBnstats) only supports data format "
           "of "
-          "channel last (NHWC) now. But recieved: data_format = '%s'.",
+          "channel last (NHWC) now. But received: data_format = '%s'.",
           data_format));
 
   PADDLE_ENFORCE_EQ(
@@ -2954,7 +2954,7 @@ void FusedConv2dAddActInferMeta(const MetaTensor& input,
             "The number of Output(Outputs) of operator 'fused_conv2d_add_act' "
             "is "
             "expected to be equal to the length of Attr(split_channels). But "
-            "reiceved: the number of Output(Outputs) = %u; the length of "
+            "received: the number of Output(Outputs) = %u; the length of "
             "Attr(split_channels) = %u, the content = [%s].",
             outputs.size(),
             split_channels.size(),
@@ -3212,7 +3212,7 @@ void FusionGRUInferMeta(const MetaTensor& x,
                       frame_size,
                       phi::errors::InvalidArgument(
                           "The width of H0 must be equal to frame_size, but "
-                          "receiced the width of H0 is:%d, frame size is:%d",
+                          "received the width of H0 is:%d, frame size is:%d",
                           h0_dims[1],
                           frame_size));
     reordered_h0->set_dtype(x.dtype());
@@ -3288,14 +3288,14 @@ void FusionSeqConvEltAddReluInferMeta(const MetaTensor& x,
       x_dims.size(),
       2,
       phi::errors::InvalidArgument(
-          "Input(X) should be 2-D tensor, but reveiced value is: %d.",
+          "Input(X) should be 2-D tensor, but received value is: %d.",
           x_dims.size()));
 
   PADDLE_ENFORCE_EQ(
       w_dims.size(),
       2,
       phi::errors::InvalidArgument(
-          "Filter should be 2-D tensor, but reveiced value is: %d.",
+          "Filter should be 2-D tensor, but received value is: %d.",
           w_dims.size()));
 
   PADDLE_ENFORCE_EQ(w_dims[0],
@@ -3594,7 +3594,7 @@ void VariableLengthMemoryEfficientAttentionInferMeta(
       0,
       errors::InvalidArgument(
           "The num_head of query must be divisible by the num_head of key, but "
-          "recived num_head of query is %d, and the num_head of key is %d",
+          "received num_head of query is %d, and the num_head of key is %d",
           query_num_head,
           key_num_head));
 
diff --git a/paddle/phi/infermeta/multiary.cc b/paddle/phi/infermeta/multiary.cc
@@ -1930,7 +1930,7 @@ void FusionGroupInferMeta(const std::vector<const MetaTensor*>& ins,
       num_outs,
       1UL,
       phi::errors::InvalidArgument(
-          "Expected the number of outputs >= 1. Recived %d.", num_outs));
+          "Expected the number of outputs >= 1. Received %d.", num_outs));
 
   PADDLE_ENFORCE_EQ(type,
                     0UL,
@@ -2149,13 +2149,13 @@ static void Interpolate1DInferShapeCheck(
     PADDLE_ENFORCE_NE(
         dim_x[i],
         0,
-        phi::errors::InvalidArgument("The shape of input(x) should be larged "
+        phi::errors::InvalidArgument("The shape of input(x) should be larger "
                                      "than 0, bug received shape[%d] is %d ",
                                      i,
                                      dim_x[i]));
   }
   if (size_tensor && !size_tensor->empty()) {
-    // top prority size
+    // top priority size
     auto inputs_name = size_tensor.get();
     PADDLE_ENFORCE_EQ(
         inputs_name.size(),
@@ -2225,7 +2225,7 @@ static void Interpolate1DInferShapeCheck(
         out_size_dim.size(),
         1,
         phi::errors::InvalidArgument(
-            "OutSize's dimension size must be 1, but got dimention = %d .",
+            "OutSize's dimension size must be 1, but got dimension = %d .",
             out_size_dim.size()));
     PADDLE_ENFORCE_EQ(
         out_size_dim[0],
@@ -2281,14 +2281,14 @@ static void Interpolate2DInferShapeCheck(
     PADDLE_ENFORCE_NE(
         dim_x[i],
         0,
-        phi::errors::InvalidArgument("The shape of input(x) should be larged "
+        phi::errors::InvalidArgument("The shape of input(x) should be larger "
                                      "than 0, bug received shape[%d] is %d ",
                                      i,
                                      dim_x[i]));
   }
 
   if (size_tensor && !size_tensor->empty()) {
-    // top prority size
+    // top priority size
     auto inputs_name = size_tensor.get();
     PADDLE_ENFORCE_EQ(
         inputs_name.size(),
@@ -2384,7 +2384,7 @@ static void Interpolate2DInferShapeCheck(
         out_size_dim[0],
         2,
         phi::errors::InvalidArgument(
-            "OutSize's dim[0] must be 2, but got dimention = %d .",
+            "OutSize's dim[0] must be 2, but got dimension = %d .",
             out_size_dim[0]));
     // dims will be seted in kernel
     output->set_dtype(x.dtype());
@@ -2434,14 +2434,14 @@ static void Interpolate3DInferShapeCheck(
     PADDLE_ENFORCE_NE(
         dim_x[i],
         0,
-        phi::errors::InvalidArgument("The shape of input(x) should be larged "
+        phi::errors::InvalidArgument("The shape of input(x) should be larger "
                                      "than 0, bug received shape[%d] is %d ",
                                      i,
                                      dim_x[i]));
   }
 
   if (size_tensor && !size_tensor->empty()) {
-    // top prority size
+    // top priority size
     auto inputs_name = size_tensor.get();
     PADDLE_ENFORCE_EQ(
         inputs_name.size(),
diff --git a/paddle/phi/infermeta/spmd_rules/dim_trans.h b/paddle/phi/infermeta/spmd_rules/dim_trans.h
@@ -57,7 +57,7 @@ class DimTrans {
   Type type_;
 };
 
-// InputDim indicates that the output dimention
+// InputDim indicates that the output dimension
 // is obtained directed from one input dimension.
 class InputDim : public DimTrans {
  public:
diff --git a/paddle/phi/infermeta/spmd_rules/fused_rope.cc b/paddle/phi/infermeta/spmd_rules/fused_rope.cc
@@ -87,7 +87,7 @@ void check_sin_cos(const DistMetaTensor& sin,
                     cos.dims(),
                     phi::errors::InvalidArgument(
                         "The dims of sin and cos must be the same. But "
-                        "recieved sin's dims is {%s}, cos's dims is {%s}.",
+                        "received sin's dims is {%s}, cos's dims is {%s}.",
                         sin.dims(),
                         sin.dims()));
 
@@ -140,7 +140,7 @@ void check_sin_cos(const DistMetaTensor& sin,
         true,
         phi::errors::InvalidArgument(
             "The batch_size and seq_len of position_ids must be the same as "
-            "those of q. But recieved position_ids's "
+            "those of q. But received position_ids's "
             "shape is {%s}, q's shape is {%s}.",
             str_join(position_ids_shape),
             str_join(q_shape)));
diff --git a/paddle/phi/infermeta/spmd_rules/optimizer.cc b/paddle/phi/infermeta/spmd_rules/optimizer.cc
@@ -51,7 +51,7 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param,
   // Do spmd infer on param and grad in case of the param and grad
   // has different dist attr. This difference may be caused by other spmd.
   // No need do the spmd infer on the two momentum, since they are
-  // seperated from the forward backward computation.
+  // separated from the forward backward computation.
   SpmdInfo param_grad_spmd = ElementwiseBinaryInferSpmd(param, grad);
   TensorDistAttr param_dist_attr_spmd =
       PADDLE_GET(TensorDistAttr, param_grad_spmd.first[0]);
diff --git a/paddle/phi/infermeta/spmd_rules/utils.h b/paddle/phi/infermeta/spmd_rules/utils.h
@@ -34,15 +34,15 @@ inline bool IsEmpty(const std::vector<int64_t>& shape) {
 
 // Generate the axis notation of tensor for the einsum notation of a broadcast
 // operation(alignment star from the rightmost axis). tensor_ndim: the size of
-// the tensor. broadcast_ndim: the maxium size of tensors in this broadcast
+// the tensor. broadcast_ndim: the maximum size of tensors in this broadcast
 // operation. alphabet: the characters used to represent the axes of tensor.
 // length of alphabet should >= broadcast_ndim.
 std::string GetBroadcastAxes(const int64_t& tensor_ndim,
                              const int64_t& broadcast_ndim,
                              const std::string& alphabet);
 
 // Merge the sharding specification (dims mapping) for one tensor Axis.
-// Rule1: A repicated dimension could be merged by any sharded dimension.
+// Rule1: A replicated dimension could be merged by any sharded dimension.
 // Rule2: A tensor axis could at most be sharded by one mesh dimension.
 // (TODO trigger heuristics cost model and reshard to handle axis sharded by
 // multiple dimension case.)
@@ -67,14 +67,14 @@ TensorDistAttr UnShardTensorDims(const TensorDistAttr& dist_attr,
                                  std::vector<int64_t> dims);
 
 // Resolute the partial mesh dimension of a output tensor, giving the
-// merged sharding specifcation of input tensors and the axis names of output
+// merged sharding specification of input tensors and the axis names of output
 // tensor. Input are
 std::vector<int64_t> ResoluteOutputPartialDimension(
     const std::unordered_map<std::string, int64_t>& axis_to_dim_map,
     const std::string& tensor_axes);
 
 // Construct a DistAttr from the incoming DistAttr corresponding to the
-// Repliacated state
+// Replicated state
 TensorDistAttr GetReplicatedDistAttr(const TensorDistAttr& dist_attr);
 
 bool IsDimSharded(const TensorDistAttr& dist_attr, int dim);
diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
