update conv kernel

CMakeLists.txt

@@ -16,7 +16,6 @@ file(GLOB_RECURSE PADDLE_MOBILE_CC src/*.cc src/*.cpp src/*.c src/*.mm)
 file(GLOB_RECURSE PADDLE_MOBILE_H src/*.h)
 include_directories(src/)
 
-
 if(IS_IOS)
     set(CMAKE_CXX_FLAGS "-mfpu=neon -marm -fobjc-abi-version=2 -fobjc-arc -std=gnu++11 -stdlib=libc++ -O3 -s -isysroot ${CMAKE_OSX_SYSROOT} ${CMAKE_CXX_FLAGS}")
 else()

src/framework/cl/cl_half.cpp

@@ -16,6 +16,9 @@ limitations under the License. */
 
 #include "framework/cl/cl_half.h"
 
+namespace paddle_mobile {
+namespace framework {
+
 static const uint32_t mantissatable[2048] = {
     0x00000000, 0x33800000, 0x34000000, 0x34400000, 0x34800000, 0x34a00000,
     0x34c00000, 0x34e00000, 0x35000000, 0x35100000, 0x35200000, 0x35300000,
@@ -510,3 +513,6 @@ void HalfArray2FloatArray(half_t *h_array, float *f_array, int count) {
     f_array[i] = Half2Float(h_array[i]);
   }
 }
+
+}  // namespace framework
+}  // namespace paddle_mobile

src/framework/cl/cl_half.h

@@ -15,6 +15,9 @@ limitations under the License. */
 #pragma once
 #include <cstdint>
 
+namespace paddle_mobile {
+namespace framework {
+
 typedef uint16_t half_t;
 
 half_t Float2Half(float f);
@@ -24,3 +27,6 @@ float Half2Float(half_t h);
 void FloatArray2HalfArray(float *f_array, half_t *h_array, int count);
 
 void HalfArray2FloatArray(half_t *h_array, float *f_array, int count);
+
+}  // namespace framework
+}  // namespace paddle_mobile

src/framework/cl/cl_image.cpp

@@ -12,7 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License. */
 
-#include "cl_image.h"
+#include "framework/cl/cl_image.h"
+
 namespace paddle_mobile {
 namespace framework {
 void CLImageToTensor(CLImage *cl_image, Tensor *tensor,
@@ -63,7 +64,7 @@ void CLImageToTensor(CLImage *cl_image, Tensor *tensor,
   }
 
   if (err != CL_SUCCESS) {
-    // TODO: error handling
+    CL_CHECK_ERRORS(err);
   }
 }
 void TensorToCLImage(const Tensor *tensor, CLImage *cl_image,
@@ -97,7 +98,7 @@ void TensorToCLImage(const Tensor *tensor, CLImage *cl_image,
   err = clEnqueueReadImage(commandQueue, image, CL_TRUE, origin, region, 0, 0,
                            imageData, 0, NULL, NULL);
   if (err != CL_SUCCESS) {
-    // TODO: error handling
+    CL_CHECK_ERRORS(err);
   }
   size_t i0 = 0;
   for (int n = 0; n < N; n++) {
@@ -117,7 +118,7 @@ void TensorToCLImage(const Tensor *tensor, CLImage *cl_image,
   }
 }
 #ifdef PADDLE_MOBILE_DEBUG
-Print &operator<<(Print &printer, const CLImage &cl_image){
+Print &operator<<(Print &printer, const CLImage &cl_image) {
   printer << " dims: " << cl_image.dims() << "\n";
   int stride = cl_image.numel() / 20;
   stride = stride > 0 ? stride : 1;
@@ -148,8 +149,8 @@ Print &operator<<(Print &printer, const CLImage &cl_image){
   cl_mem image = cl_image.GetCLImage();
   size_t origin[3] = {0, 0, 0};
   size_t region[3] = {width, height, 1};
-  err = clEnqueueReadImage(cl_image.CommandQueue(), image, CL_TRUE, origin, region, 0, 0,
-                           imageData, 0, NULL, NULL);
+  err = clEnqueueReadImage(cl_image.CommandQueue(), image, CL_TRUE, origin,
+                           region, 0, 0, imageData, 0, NULL, NULL);
   size_t i0 = 0;
   for (int n = 0; n < N; n++) {
     for (int c = 0; c < C; c++) {
@@ -168,13 +169,13 @@ Print &operator<<(Print &printer, const CLImage &cl_image){
   }
 
   if (err != CL_SUCCESS) {
-    // TODO: error handling
+    CL_CHECK_ERRORS(err);
+  }
+  for (int i = 0; i < cl_image.numel(); i += stride) {
+    printer << data[i] << " ";
   }
-    for (int i = 0; i < cl_image.numel(); i += stride) {
-            printer << data[i] << " ";
-    }
   return printer;
-        }
+}
 #endif
 }  // namespace framework
 }  // namespace paddle_mobile

src/framework/cl/cl_image.h

@@ -46,27 +46,28 @@ class CLImage {
   /*
    * need call SetTensorData first
    * */
-  void InitCLImage(cl_context context,cl_command_queue command_queue) {
+  void InitCLImage(cl_context context, cl_command_queue command_queue) {
     if (tensor_data_ == nullptr) {
       PADDLE_MOBILE_THROW_EXCEPTION(" need call SetTensorData first");
     }
     if (tensor_dims_.size() <= 2) {
-      InitCLImage2C(context, command_queue,tensor_data_, tensor_dims_);
+      InitCLImage2C(context, command_queue, tensor_data_, tensor_dims_);
     } else {
-      InitCLImage(context, command_queue,tensor_data_, tensor_dims_);
+      InitCLImage(context, command_queue, tensor_data_, tensor_dims_);
     }
     delete[](tensor_data_);
     tensor_data_ = nullptr;
     initialized_ = true;
   }
 
-  void InitEmptyImage(cl_context context, cl_command_queue command_queue,const DDim &dim) {
+  void InitEmptyImage(cl_context context, cl_command_queue command_queue,
+                      const DDim &dim) {
     if (tensor_data_ != nullptr) {
       PADDLE_MOBILE_THROW_EXCEPTION(
           " empty image tensor data shouldn't have value");
     }
     DLOG << " init empty image ";
-    InitCLImage(context, command_queue,nullptr, dim);
+    InitCLImage(context, command_queue, nullptr, dim);
     initialized_ = true;
   }
 
@@ -93,7 +94,7 @@ class CLImage {
    * */
   inline size_t HeightOfOneBlock() const { return height_of_one_block_; }
 
-  inline cl_command_queue  CommandQueue() const{ return command_queue_;}
+  inline cl_command_queue CommandQueue() const { return command_queue_; }
 
   /*
    *  resize original tensor dim
@@ -124,7 +125,8 @@ class CLImage {
   const DDim &dims() const { return tensor_dims_; }
 
  private:
-  void InitCLImage2C(cl_context context, cl_command_queue command_queue,float *tensor_data, const DDim &dim) {
+  void InitCLImage2C(cl_context context, cl_command_queue command_queue,
+                     float *tensor_data, const DDim &dim) {
     command_queue_ = command_queue;
     assert(dim.size() <= 2);
     int tdim[2] = {1, 1};
@@ -141,43 +143,44 @@ class CLImage {
       imageData.reset(new half_t[width * height * 4]);
       for (int h = 0; h < tdim[0]; h++) {
         for (int w = 0; w < tdim[1]; w++) {
-          imageData[(h * width + w / 4) * 4 + (w % 4)] = Float2Half(tensor_data[h * tdim[1] + w]);
+          imageData[(h * width + w / 4) * 4 + (w % 4)] =
+              Float2Half(tensor_data[h * tdim[1] + w]);
         }
       }
     }
     InitCLImage(context, width, height, imageData.get());
   }
 
-  void InitCLImage(cl_context context,int width, int height, void *data) {
+  void InitCLImage(cl_context context, int width, int height, void *data) {
     cl_image_format cf = {.image_channel_order = CL_RGBA,
                           .image_channel_data_type = CL_HALF_FLOAT};
     cl_image_desc cid = {
-      .image_type = CL_MEM_OBJECT_IMAGE2D,
-      .image_width = width,
-      .image_height = height,
-      .image_depth = 1,
-      .image_array_size = 1,
-      .image_row_pitch = 0,
-      .image_slice_pitch = 0,
-      .num_mip_levels = 0,
-      .num_samples = 0,
-      // .buffer = nullptr
+        .image_type = CL_MEM_OBJECT_IMAGE2D,
+        .image_width = width,
+        .image_height = height,
+        .image_depth = 1,
+        .image_array_size = 1,
+        .image_row_pitch = 0,
+        .image_slice_pitch = 0,
+        .num_mip_levels = 0,
+        .num_samples = 0,
+        // .buffer = nullptr
     };
     cid.buffer = nullptr;
     cl_int err;
     cl_image_ = clCreateImage(
-      context, CL_MEM_READ_WRITE | (data ? CL_MEM_COPY_HOST_PTR : 0),
-      &cf,   // const cl_image_format *image_format
-      &cid,  // const cl_image_desc *image_desc
-      data,  // void *host_ptr
-      &err
-    );
+        context, CL_MEM_READ_WRITE | (data ? CL_MEM_COPY_HOST_PTR : 0),
+        &cf,   // const cl_image_format *image_format
+        &cid,  // const cl_image_desc *image_desc
+        data,  // void *host_ptr
+        &err);
     if (err != CL_SUCCESS) {
       CL_CHECK_ERRORS(err);
       PADDLE_MOBILE_THROW_EXCEPTION(" create image 2d error ");
     }
   }
-  void InitCLImage(cl_context context, cl_command_queue command_queue,float *tensor_data, const DDim &dim) {
+  void InitCLImage(cl_context context, cl_command_queue command_queue,
+                   float *tensor_data, const DDim &dim) {
     DLOG << " tensor dim: " << dim;
     // NCHW -> [W * (C+3)/4, H * N]
     tensor_dims_ = dim;
@@ -207,6 +210,7 @@ class CLImage {
     image_width_ = width;
     image_height_ = height;
     image_dims_ = make_ddim({image_width_, image_height_});
+    c_block_ = W / width;
 
     std::unique_ptr<half_t[]> imageData{};
     int count = 0;

src/framework/executor.cpp

@@ -37,7 +37,7 @@ limitations under the License. */
 #include "framework/cl/cl_image.h"
 #endif
 
-int debug_to = 115;
+int debug_to = 3;
 
 namespace paddle_mobile {
 namespace framework {
@@ -953,13 +953,14 @@ void Executor<GPU_CL, Precision::FP32>::InitMemory() {
         if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
           auto cl_image = var->template GetMutable<framework::CLImage>();
           cl_context context = program_.scope->GetCLScpoe()->Context();
-          cl_command_queue command_queue = program_.scope->GetCLScpoe()->CommandQueue();
+          cl_command_queue command_queue =
+              program_.scope->GetCLScpoe()->CommandQueue();
 
           const framework::TensorDesc &desc = var_desc->Tensor_desc();
           //          framework::DDim ddim = framework::make_ddim(desc.Dims());
           framework::DDim ddim = cl_image->dims();
           DLOG << var_desc->Name();
-          cl_image->InitEmptyImage(context,command_queue, ddim);
+          cl_image->InitEmptyImage(context, command_queue, ddim);
         }
       }
     }
@@ -1011,11 +1012,12 @@ void Executor<GPU_CL, Precision::FP32>::InitCombineMemory() {
       } else {
         auto cl_image = var->template GetMutable<framework::CLImage>();
         cl_context context = program_.scope->GetCLScpoe()->Context();
-        cl_command_queue command_queue = program_.scope->GetCLScpoe()->CommandQueue();
+        cl_command_queue command_queue =
+            program_.scope->GetCLScpoe()->CommandQueue();
         const framework::TensorDesc &desc = var_desc->Tensor_desc();
         framework::DDim ddim = cl_image->dims();
         //        framework::DDim ddim = framework::make_ddim(desc.Dims());
-        cl_image->InitEmptyImage(context, command_queue,ddim);
+        cl_image->InitEmptyImage(context, command_queue, ddim);
       }
     }
   }

src/framework/operator.cpp

@@ -73,7 +73,7 @@ void OperatorBase<Dtype>::Run() {
         } else {
           CLImage *cl_image = vari->template GetMutable<framework::CLImage>();
           if (cl_image) {
-            DLOG << type_ << " input- " << key << "="<<*cl_image;
+            DLOG << type_ << " input- " << key << "=" << *cl_image;
           }
         }
 
@@ -98,7 +98,7 @@ void OperatorBase<Dtype>::Run() {
         } else {
           CLImage *cl_image = vari->template GetMutable<framework::CLImage>();
           if (cl_image) {
-            DLOG << type_ << " output- " << key << "="<<*cl_image ;
+            DLOG << type_ << " output- " << key << "=" << *cl_image;
           }
         }
 

src/operators/kernel/cl/batchnorm_kernel.cpp

@@ -49,11 +49,13 @@ bool BatchNormKernel<GPU_CL, float>::Init(BatchNormParam<GPU_CL> *param) {
 
   framework::CLImage *new_scale = new framework::CLImage();
   new_scale->SetTensorData(new_scale_ptr, variance->dims());
-  new_scale->InitCLImage(this->cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
+  new_scale->InitCLImage(this->cl_helper_.CLContext(),
+                         this->cl_helper_.CLCommandQueue());
 
   framework::CLImage *new_bias = new framework::CLImage();
   new_bias->SetTensorData(new_bias_ptr, variance->dims());
-  new_bias->InitCLImage(this->cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
+  new_bias->InitCLImage(this->cl_helper_.CLContext(),
+                        this->cl_helper_.CLCommandQueue());
 
   param->SetNewScale(new_scale);
   param->SetNewBias(new_bias);

src/operators/kernel/cl/cl_kernel/conv_kernel.cl

@@ -19,6 +19,7 @@ __kernel void conv_3x3(__private const int global_size_dim0,
                                               __private const int global_size_dim2,
                                               __read_only image2d_t input_image,
                                               __read_only image2d_t filter,
+
 #ifdef BIASE
                                               __read_only image2d_t bias,
 #endif

src/operators/kernel/cl/conv_add_bn_relu_kernel.cpp

@@ -29,8 +29,10 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
           param->Paddings()[0] == param->Paddings()[1],
       "need equal");
 
-  param->Filter()->InitCLImage(cl_helper_.CLContext(),cl_helper_.CLCommandQueue());
-  param->Bias()->InitCLImage(cl_helper_.CLContext(),cl_helper_.CLCommandQueue());
+  param->Filter()->InitCLImage(cl_helper_.CLContext(),
+                               cl_helper_.CLCommandQueue());
+  param->Bias()->InitCLImage(cl_helper_.CLContext(),
+                             cl_helper_.CLCommandQueue());
 
   //  const CL *mean = param->InputMean();
   const framework::CLImage *mean = param->InputMean();
@@ -62,12 +64,14 @@ bool ConvAddBNReluKernel<GPU_CL, float>::Init(
   framework::CLImage *new_scale = new framework::CLImage();
 
   new_scale->SetTensorData(new_scale_ptr, variance->dims());
-  new_scale->InitCLImage(this->cl_helper_.CLContext(),cl_helper_.CLCommandQueue());
+  new_scale->InitCLImage(this->cl_helper_.CLContext(),
+                         cl_helper_.CLCommandQueue());
 
   framework::CLImage *new_bias = new framework::CLImage();
 
   new_bias->SetTensorData(new_bias_ptr, variance->dims());
-  new_bias->InitCLImage(this->cl_helper_.CLContext(),cl_helper_.CLCommandQueue());
+  new_bias->InitCLImage(this->cl_helper_.CLContext(),
+                        cl_helper_.CLCommandQueue());
 
   param->SetNewScale(new_scale);
   param->SetNewBias(new_bias);

src/operators/kernel/cl/conv_add_kernel.cpp

@@ -25,8 +25,10 @@ bool ConvAddKernel<GPU_CL, float>::Init(FusionConvAddParam<GPU_CL> *param) {
       param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
           param->Paddings()[0] == param->Paddings()[1],
       "need equal");
-  param->Filter()->InitCLImage(cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
-  param->Bias()->InitCLImage(cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
+  param->Filter()->InitCLImage(cl_helper_.CLContext(),
+                               this->cl_helper_.CLCommandQueue());
+  param->Bias()->InitCLImage(cl_helper_.CLContext(),
+                             this->cl_helper_.CLCommandQueue());
 
   int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
                static_cast<int>(param->Paddings()[1]);

src/operators/kernel/cl/conv_kernel.cpp

@@ -26,7 +26,8 @@ bool ConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
           param->Paddings()[0] == param->Paddings()[1],
       "need equal");
 
-  param->Filter()->InitCLImage(cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
+  param->Filter()->InitCLImage(cl_helper_.CLContext(),
+                               this->cl_helper_.CLCommandQueue());
 
   int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
                static_cast<int>(param->Paddings()[1]);
@@ -95,6 +96,17 @@ void ConvKernel<GPU_CL, float>::Compute(const ConvParam<GPU_CL> &param) {
   cl_int status;
 
   DLOG << " begin set kernel arg ";
+  DLOG << " c block " << c_block;
+  DLOG << " w " << w;
+  DLOG << " nh " << nh;
+  DLOG << " stride " << stride;
+  DLOG << " offset " << offset;
+  DLOG << " input_c " << input_c;
+  DLOG << " dilation " << dilation;
+  DLOG << " input width " << input_width;
+  DLOG << " input height " << input_height;
+  DLOG << " output width " << output_width;
+  DLOG << " output height " << output_height;
 
   status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
   CL_CHECK_ERRORS(status);

src/operators/kernel/cl/depthwise_conv_kernel.cpp

@@ -27,7 +27,8 @@ bool DepthwiseConvKernel<GPU_CL, float>::Init(ConvParam<GPU_CL> *param) {
       param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
           param->Paddings()[0] == param->Paddings()[1],
       "need equal");
-  param->Filter()->InitCLImage(cl_helper_.CLContext(),this->cl_helper_.CLCommandQueue());
+  param->Filter()->InitCLImage(cl_helper_.CLContext(),
+                               this->cl_helper_.CLCommandQueue());
   int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
                static_cast<int>(param->Paddings()[1]);
   param->SetOffset(offset);

src/operators/kernel/cl/feed_kernel.cpp

@@ -30,7 +30,7 @@ void FeedKernel<GPU_CL, float>::Compute(const FeedParam<GPU_CL> &param) {
   cl_int status;
   auto output = param.Out();
   const Tensor *input = param.InputX();
-    DLOG<<*input;
+  DLOG << *input;
   const float *input_data = input->data<float>();
   int numel = input->numel();
   cl_mem cl_image = output->GetCLImage();

src/operators/kernel/cl/fetch_kernel.cpp

@@ -19,44 +19,45 @@ namespace operators {
 
 template <>
 bool FetchKernel<GPU_CL, float>::Init(FetchParam<GPU_CL> *param) {
-//  this->cl_helper_.AddKernel("fetch", "fetch_kernel.cl");
+  //  this->cl_helper_.AddKernel("fetch", "fetch_kernel.cl");
   return true;
 }
 
 template <>
 void FetchKernel<GPU_CL, float>::Compute(const FetchParam<GPU_CL> &param) {
-//  auto kernel = this->cl_helper_.KernelAt(0);
-//  auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.InputX());
-//
-//  auto input = param.InputX()->GetCLImage();
-//  auto *out = param.Out();
-//
-//  const auto &dims = param.InputX()->dims();
-//  const int N = dims[0];
-//  const int C = dims[1];
-//  const int in_height = dims[2];
-//  const int in_width = dims[3];
-//
-//  int size_ch = in_height * in_width;
-//  int size_block = size_ch * 4;
-//  int size_batch = size_ch * C;
-//
-//  // need create outputBuffer
-//  cl_image_format imageFormat;
-//  imageFormat.image_channel_order = CL_RGBA;
-//  imageFormat.image_channel_data_type = CL_FLOAT;
-//  cl_mem outputBuffer;
-//
-//  clSetKernelArg(kernel, 0, sizeof(int), &in_height);
-//  clSetKernelArg(kernel, 1, sizeof(int), &in_width);
-//  clSetKernelArg(kernel, 2, sizeof(int), &size_ch);
-//  clSetKernelArg(kernel, 3, sizeof(int), &size_block);
-//  clSetKernelArg(kernel, 4, sizeof(int), &size_batch);
-//  clSetKernelArg(kernel, 5, sizeof(cl_mem), &input);
-//  clSetKernelArg(kernel, 6, sizeof(cl_mem), &outputBuffer);
-//
-//  clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
-//                         default_work_size.data(), NULL, 0, NULL, NULL);
+  //  auto kernel = this->cl_helper_.KernelAt(0);
+  //  auto default_work_size =
+  //  this->cl_helper_.DefaultWorkSize(*param.InputX());
+  //
+  //  auto input = param.InputX()->GetCLImage();
+  //  auto *out = param.Out();
+  //
+  //  const auto &dims = param.InputX()->dims();
+  //  const int N = dims[0];
+  //  const int C = dims[1];
+  //  const int in_height = dims[2];
+  //  const int in_width = dims[3];
+  //
+  //  int size_ch = in_height * in_width;
+  //  int size_block = size_ch * 4;
+  //  int size_batch = size_ch * C;
+  //
+  //  // need create outputBuffer
+  //  cl_image_format imageFormat;
+  //  imageFormat.image_channel_order = CL_RGBA;
+  //  imageFormat.image_channel_data_type = CL_FLOAT;
+  //  cl_mem outputBuffer;
+  //
+  //  clSetKernelArg(kernel, 0, sizeof(int), &in_height);
+  //  clSetKernelArg(kernel, 1, sizeof(int), &in_width);
+  //  clSetKernelArg(kernel, 2, sizeof(int), &size_ch);
+  //  clSetKernelArg(kernel, 3, sizeof(int), &size_block);
+  //  clSetKernelArg(kernel, 4, sizeof(int), &size_batch);
+  //  clSetKernelArg(kernel, 5, sizeof(cl_mem), &input);
+  //  clSetKernelArg(kernel, 6, sizeof(cl_mem), &outputBuffer);
+  //
+  //  clEnqueueNDRangeKernel(this->cl_helper_.CLCommandQueue(), kernel, 3, NULL,
+  //                         default_work_size.data(), NULL, 0, NULL, NULL);
 }
 
 template class FetchKernel<GPU_CL, float>;

src/operators/kernel/cl/reshape_kernel.cpp

@@ -37,10 +37,10 @@ void ReshapeKernel<GPU_CL, float>::Compute(const ReshapeParam<GPU_CL> &param) {
   int dims[4] = {1, 1, 1, 1};
   int odims[4] = {1, 1, 1, 1};
   for (int i = 0; i < inputDim.size(); i++) {
-    dims[4-inputDim.size()+i] = inputDim[i];
+    dims[4 - inputDim.size() + i] = inputDim[i];
   }
   for (int i = 0; i < outputDim.size(); i++) {
-    odims[4-outputDim.size()+i] = outputDim[i];
+    odims[4 - outputDim.size() + i] = outputDim[i];
   }
   clSetKernelArg(kernel, 2, sizeof(cl_int), &dims);
   clSetKernelArg(kernel, 3, sizeof(cl_int), &dims[1]);

tools/pre-commit.hooks/clang-format.hook

@@ -17,7 +17,7 @@ shift
 perl -i -pe 's|^\s+#pragma\s+omp|// <TRICKY-CLANG-FORMAT-PRAGMA-FIX> #pragma omp|' "$@"
 (
 # remove clang format ios_io folder
-flist=$(echo "$@" | perl -pe 's|src/ios_io/[^ ]*||')
+flist=$(echo "$@" | perl -pe 's|src/io/ios_io/[^ ]*||')
 clang-format -i $flist
 )
 perl -i -pe 's|// <TRICKY-CLANG-FORMAT-PRAGMA-FIX> ||' "$@"