fix(mgb): format code

GitOrigin-RevId: 3e58539a2f7562c345343cfd5ed3918147b15210

dnn/src/fallback/convolution/algos.cpp

@@ -586,7 +586,7 @@ bool ConvolutionBackwardDataImpl::AlgoMatrixMul::is_preferred(
 }
 
 /* ===================== Matrix mul nchw44 algo ===================== */
-namespace{
+namespace {
 void kern_matmul_nchw44(const NCBKernParam& param) {
     bool is_xcorr = !param.filter_meta.should_flip;
     UNPACK_CONV_F32_NCB_KERN_SIZES(param);
@@ -628,7 +628,7 @@ void kern_matmul_nchw44(const NCBKernParam& param) {
     }
     TensorND B_, C_;
     for (size_t n = 0; n < N; ++n) {
-        float*C_src, *C_dst;
+        float *C_src, *C_dst;
         float* diff = const_cast<float*>(param.diff<float>() + n * param.inp_bs);
         float* grad = param.grad<float>() + n * param.out_bs;
         if (is1X1) {
@@ -637,13 +637,13 @@ void kern_matmul_nchw44(const NCBKernParam& param) {
             C_src = static_cast<float*>(bundle.get(0));
         }
         {
-            B_.layout = TensorLayout({OC/4, IH * IW, 4}, param.diff_type);
+            B_.layout = TensorLayout({OC / 4, IH * IW, 4}, param.diff_type);
             B_.reset_ptr(static_cast<void*>(diff));
             C_.layout = TensorLayout({IC / 4 * FH * FW, IH * IW, 4}, param.grad_type);
             C_.reset_ptr(C_src);
             Workspace workspace(
                     static_cast<dt_byte*>(bundle.get(1)), bundle.get_size(1));
-            auto  matmul_opr =get_matmul_opr(param);
+            auto matmul_opr = get_matmul_opr(param);
             matmul_opr->exec(A_dst, B_, C_, workspace);
         }
 

dnn/src/fallback/convolution/col2img_helper.h

@@ -86,7 +86,7 @@ void col2img_stride_padding_nchw44(
                         if (h >= 0 && h < OH && w >= 0 && w < OW) {
                             float* dst_ptr = dst + (ic * OH * OW + h * OW + w) * 4;
                             GI_FLOAT32_t dst_data = GiLoadFloat32(dst_ptr);
-                            GI_FLOAT32_t src_data = GiLoadFloat32(src+i);
+                            GI_FLOAT32_t src_data = GiLoadFloat32(src + i);
                             GiStoreFloat32(dst_ptr, GiAddFloat32(dst_data, src_data));
                         }
                         i += 4;

dnn/test/fallback/convolution.cpp

@@ -804,13 +804,11 @@ TEST_F(FALLBACK, BENCHMARK_CONVOLUTION_BACKWARD_DATA_NCHW44) {
                 .set_dtype(2, dtype::Float32{})
                 .set_times(RUN);
 
-        auto tnchw =
-                benchmarker_fallback.set_param(param)
-                        .exec(TensorLayoutArray{filter, diff, grad});
+        auto tnchw = benchmarker_fallback.set_param(param).exec(
+                TensorLayoutArray{filter, diff, grad});
         param.format = Param::Format::NCHW44;
-        auto tnchw44 =
-                benchmarker_fallback.set_param(param)
-                        .exec(TensorLayoutArray{filter_nchw44, diff_nchw44, grad_nchw44});
+        auto tnchw44 = benchmarker_fallback.set_param(param).exec(
+                TensorLayoutArray{filter_nchw44, diff_nchw44, grad_nchw44});
         size_t IC = ic;
         size_t FH = fh;
         size_t FW = fw;