Fix tranposed_input transposed_input (#60989)

co63oc · web-flow · commit debcec3f5db3 · 2024-01-22T10:48:55.000+08:00
diff --git a/paddle/phi/kernels/cpu/eig.h b/paddle/phi/kernels/cpu/eig.h
@@ -218,7 +218,7 @@ void ApplyEigKernel(const DenseTensor& input,
   int num_dims = input.dims().size();
 
   // transfer to column-major memory layout i.e. common::make_ddim from
-  // tranposed_input: [batch,row,col]->[batch,col,row]
+  // transposed_input: [batch,row,col]->[batch,col,row]
   TransposeTwoAxis<T, Context>(
       input, &input_column_major, num_dims - 1, num_dims - 2, dev_ctx);
   // make sure 'vectors_row_major' holds memory before passed to LapackEig()
diff --git a/paddle/phi/kernels/cpu/embedding_grad_kernel.cc b/paddle/phi/kernels/cpu/embedding_grad_kernel.cc
@@ -115,7 +115,7 @@ void EmbeddingGradKernel(const Context& ctx,
     functor.template apply<int64_t>();
   } else {
     PADDLE_THROW(phi::errors::Unimplemented(
-        "emebdding input only support int32 and int64"));
+        "embedding input only support int32 and int64"));
   }
 }
 
@@ -196,7 +196,7 @@ void EmbeddingSparseGradKernel(const Context& ctx,
     functor.template apply<int64_t>();
   } else {
     PADDLE_THROW(phi::errors::Unimplemented(
-        "emebdding input only support int32 and int64"));
+        "embedding input only support int32 and int64"));
   }
 }
 
diff --git a/paddle/phi/kernels/cpu/matrix_nms_kernel.cc b/paddle/phi/kernels/cpu/matrix_nms_kernel.cc
@@ -24,7 +24,7 @@ namespace phi {
 template <class T>
 static inline T BBoxArea(const T* box, const bool normalized) {
   if (box[2] < box[0] || box[3] < box[1]) {
-    // If coordinate values are is invalid
+    // If coordinate values are invalid
     // (e.g. xmax < xmin or ymax < ymin), return 0.
     return static_cast<T>(0.);
   } else {
diff --git a/paddle/phi/kernels/cpu/mode_grad_kernel.cc b/paddle/phi/kernels/cpu/mode_grad_kernel.cc
@@ -147,7 +147,7 @@ void ModeGradKernel(const Context& dev_ctx,
         common::slice_ddim(trans_in_shape, 0, trans_in_shape.size() - 1));
     const int64_t input_width = trans_in_shape[trans_in_shape.size() - 1];
 
-    // Assign the out_grad to tranpose input_grad
+    // Assign the out_grad to transpose input_grad
     DenseTensor tmp_out;
     tmp_out.Resize(trans_in_shape);
     T* t_out = dev_ctx.template Alloc<T>(&tmp_out);
diff --git a/paddle/phi/kernels/cpu/mode_kernel.cc b/paddle/phi/kernels/cpu/mode_kernel.cc
@@ -49,7 +49,7 @@ void ModeKernel(const Context& dev_ctx,
   }
 
   // if axis is not the last dim, transpose it to the last dim, do the
-  // calculation, then tranpose it back to original axis.
+  // calculation, then transpose it back to original axis.
   if (axis == in_dims.size() - 1) {
     const int64_t& input_height =
         common::product(common::slice_ddim(in_dims, 0, in_dims.size() - 1));
diff --git a/paddle/phi/kernels/cpu/svd_kernel.cc b/paddle/phi/kernels/cpu/svd_kernel.cc
@@ -121,7 +121,7 @@ void SvdKernel(const Context& dev_ctx,
   BatchSvd<T>(x_data, U_out, VH_out, S_out, rows, cols, batches, full);
   /* let C[m, n] as a col major matrix with m rows and n cols.
    * let R[m, n] is row major matrix with m rows and n cols.
-   * then we have: R[m,n] = C[m, n].resize((n,m)).tranpose_last_two()
+   * then we have: R[m,n] = C[m, n].resize((n,m)).transpose_last_two()
    * */
   auto col_major_to_row_major = [&dev_ctx](DenseTensor* out) {
     auto origin_dim = out->dims();
diff --git a/paddle/phi/kernels/cpu/top_k_grad_kernel.cc b/paddle/phi/kernels/cpu/top_k_grad_kernel.cc
@@ -124,7 +124,7 @@ void TopkGradKernel(const Context& dev_ctx,
         common::slice_ddim(trans_in_dims, 0, trans_in_dims.size() - 1));
     const int64_t input_width = trans_in_dims[trans_in_dims.size() - 1];
 
-    // Assign the out_grad to tranpose input_grad
+    // Assign the out_grad to transpose input_grad
     DenseTensor tmp_out;
     tmp_out.Resize(trans_in_dims);
     T* t_out = dev_ctx.template Alloc<T>(&tmp_out);
diff --git a/paddle/phi/kernels/cpu/top_k_kernel.cc b/paddle/phi/kernels/cpu/top_k_kernel.cc
@@ -184,7 +184,7 @@ void TopkKernel(const Context& dev_ctx,
                          largest,
                          sorted);
   } else {
-    // if the topk dims is not last dim, will tranpose and do topk
+    // if the topk dims is not last dim, will transpose and do topk
     std::vector<int> trans;
     for (int i = 0; i < axis; i++) {
       trans.emplace_back(i);
diff --git a/paddle/phi/kernels/gpu/mode_kernel.cu b/paddle/phi/kernels/gpu/mode_kernel.cu
@@ -93,7 +93,7 @@ void ModeKernel(const Context& dev_ctx,
     }
     trans_out_shape[in_dims.size() - 1] = 1;
 
-    // second step, tranpose the input
+    // second step, transpose the input
     DenseTensor trans_input;
     trans_input.Resize(trans_shape);
     dev_ctx.template Alloc<T>(&trans_input);
@@ -118,7 +118,7 @@ void ModeKernel(const Context& dev_ctx,
                             input_height,
                             trans_out_data,
                             trans_ind_data);
-    // last step, tranpose back the indices and output
+    // last step, transpose back the indices and output
     funcs::TransCompute<Context, int64_t>(
         ndims, dev_ctx, trans_ind, indices, trans_axis);
     funcs::TransCompute<Context, T>(ndims, dev_ctx, trans_out, out, trans_axis);
diff --git a/paddle/phi/kernels/gpu/top_k_kernel.cu b/paddle/phi/kernels/gpu/top_k_kernel.cu
@@ -228,10 +228,10 @@ void TopkKernel(const Context& dev_ctx,
             "the input data shape has error in the topk cuda kernel."));
     }
   } else {
-    // if get topK not from the last axis, will tranpose the tensor and get
+    // if get topK not from the last axis, will transpose the tensor and get
     // TopK
 
-    // first step, prepare the trans args for the tranpose
+    // first step, prepare the trans args for the transpose
     std::vector<int> trans;
     for (int i = 0; i < axis; i++) {
       trans.emplace_back(i);
@@ -248,7 +248,7 @@ void TopkKernel(const Context& dev_ctx,
       trans_dims[i] = in_dims[trans[i]];
       trans_out_dims[i] = out_dims[trans[i]];
     }
-    // second step, tranpose the input
+    // second step, transpose the input
     DenseTensor trans_input;
     trans_input.Resize(trans_dims);
     dev_ctx.template Alloc<T>(&trans_input);
@@ -282,7 +282,7 @@ void TopkKernel(const Context& dev_ctx,
                                   &trans_out,
                                   &trans_ind,
                                   largest)) {
-        // last step, tranpose back the indices and output
+        // last step, transpose back the indices and output
         funcs::TransCompute<phi::GPUContext, int64_t>(
             ndims, dev_ctx, trans_ind, indices, trans);
         funcs::TransCompute<phi::GPUContext, T>(
@@ -337,7 +337,7 @@ void TopkKernel(const Context& dev_ctx,
             "the input data shape has error in the topk cuda kernel."));
     }
 
-    // last step, tranpose back the indices and output
+    // last step, transpose back the indices and output
     funcs::TransCompute<phi::GPUContext, int64_t>(
         ndims, dev_ctx, trans_ind, indices, trans);
     funcs::TransCompute<phi::GPUContext, T>(

Original file line number	Diff line number	Diff line change
`@@ -115,7 +115,7 @@ void EmbeddingGradKernel(const Context& ctx,`
`115`	`115`	`functor.template apply<int64_t>();`
`116`	`116`	`} else {`
`117`	`117`	`PADDLE_THROW(phi::errors::Unimplemented(`
`118`		`- "emebdding input only support int32 and int64"));`
	`118`	`+ "embedding input only support int32 and int64"));`
`119`	`119`	`}`
`120`	`120`	`}`
`121`	`121`
`@@ -196,7 +196,7 @@ void EmbeddingSparseGradKernel(const Context& ctx,`
`196`	`196`	`functor.template apply<int64_t>();`
`197`	`197`	`} else {`
`198`	`198`	`PADDLE_THROW(phi::errors::Unimplemented(`
`199`		`- "emebdding input only support int32 and int64"));`
	`199`	`+ "embedding input only support int32 and int64"));`
`200`	`200`	`}`
`201`	`201`	`}`
`202`	`202`
Original file line number	Diff line number	Diff line change
`@@ -49,7 +49,7 @@ void ModeKernel(const Context& dev_ctx,`
`49`	`49`	`}`
`50`	`50`
`51`	`51`	`// if axis is not the last dim, transpose it to the last dim, do the`
`52`		`- // calculation, then tranpose it back to original axis.`
	`52`	`+ // calculation, then transpose it back to original axis.`
`53`	`53`	`if (axis == in_dims.size() - 1) {`
`54`	`54`	`const int64_t& input_height =`
`55`	`55`	`common::product(common::slice_ddim(in_dims, 0, in_dims.size() - 1));`