clang format

src/targets/gpu/include/migraphx/gpu/int8_gemm_pack.hpp

@@ -15,8 +15,7 @@ struct hip_int8_gemm_pack_a
 {
     std::string name() const { return "gpu::int8_gemm_pack_a"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
-    argument
-    compute(context& ctx, const shape&, const std::vector<argument>& args) const;
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
     std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {
         return shapes.size() - 1;
@@ -27,8 +26,7 @@ struct hip_int8_gemm_pack_b
 {
     std::string name() const { return "gpu::int8_gemm_pack_b"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
-    argument
-    compute(context& ctx, const shape&, const std::vector<argument>& args) const;
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
     std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
     {
         return shapes.size() - 1;

src/targets/gpu/int8_conv_pack.cpp

@@ -12,8 +12,8 @@ shape miopen_int8_conv_pack::compute_shape(const std::vector<shape>& inputs) con
 }
 
 argument miopen_int8_conv_pack::compute(context& ctx,
-                                 const shape& output_shape,
-                                 const std::vector<argument>& args) const
+                                        const shape& output_shape,
+                                        const std::vector<argument>& args) const
 {
     auto arg_desc      = make_tensor(args[0].get_shape());
     auto arg_desc_vec4 = make_tensor(args[0].get_shape(), true);

src/targets/gpu/int8_gemm_pack.cpp

@@ -12,9 +12,8 @@ shape hip_int8_gemm_pack_a::compute_shape(const std::vector<shape>& inputs) cons
     return inputs.at(0);
 }
 
-argument hip_int8_gemm_pack_a::compute(context& ctx,
-                                 const shape&,
-                                 const std::vector<argument>& args) const
+argument
+hip_int8_gemm_pack_a::compute(context& ctx, const shape&, const std::vector<argument>& args) const
 {
     device::int8_gemm_pack_a(ctx.get_stream().get(), args[1], args[0]);
     return args[1];
@@ -26,9 +25,8 @@ shape hip_int8_gemm_pack_b::compute_shape(const std::vector<shape>& inputs) cons
     return inputs.at(0);
 }
 
-argument hip_int8_gemm_pack_b::compute(context& ctx,
-                                 const shape&,
-                                 const std::vector<argument>& args) const
+argument
+hip_int8_gemm_pack_b::compute(context& ctx, const shape&, const std::vector<argument>& args) const
 {
     device::int8_gemm_pack_b(ctx.get_stream().get(), args[1], args[0]);
     return args[1];

src/targets/gpu/lowering.cpp

@@ -133,7 +133,7 @@ struct miopen_apply
         add_lrn_op();
         add_convolution_op();
         add_quant_convolution_op();
-        //add_quant_dot_op();
+        // add_quant_dot_op();
         add_pooling_op();
         add_batch_norm_inference_op();
     }
@@ -187,12 +187,14 @@ struct miopen_apply
             auto conv = miopen_quant_convolution{op, make_conv(op)};
             auto ws   = conv.compile(ctx, ins->get_shape(), to_shapes(ins->inputs()));
 
-            auto args = ins->inputs();
+            auto args       = ins->inputs();
             auto arg_x_vec4 = insert_allocation(ins, conv.pack_int8_shape(args[0]->get_shape()));
-            auto arg_x_packed = prog->insert_instruction(ins, miopen_int8_conv_pack{}, {args[0], arg_x_vec4});
+            auto arg_x_packed =
+                prog->insert_instruction(ins, miopen_int8_conv_pack{}, {args[0], arg_x_vec4});
 
             auto arg_y_vec4 = insert_allocation(ins, conv.pack_int8_shape(args[1]->get_shape()));
-            auto arg_y_packed = prog->insert_instruction(ins, miopen_int8_conv_pack{}, {args[1], arg_y_vec4});
+            auto arg_y_packed =
+                prog->insert_instruction(ins, miopen_int8_conv_pack{}, {args[1], arg_y_vec4});
 
             auto workspace = insert_allocation(ins, ws, "workspace");
             auto output    = insert_allocation(ins, ins->get_shape());

src/targets/gpu/quant_convolution.cpp

@@ -16,26 +16,26 @@ argument miopen_quant_convolution::compute(context& ctx,
                                            const shape& output_shape,
                                            const std::vector<argument>& args) const
 {
-    auto x_desc      = make_tensor(args[0].get_shape(), true);
-    auto w_desc      = make_tensor(args[1].get_shape(), true);
-    auto y_desc      = make_tensor(output_shape);
+    auto x_desc = make_tensor(args[0].get_shape(), true);
+    auto w_desc = make_tensor(args[1].get_shape(), true);
+    auto y_desc = make_tensor(output_shape);
 
     float alpha = 1;
     float beta  = 0;
 
     auto status = miopenConvolutionForward(ctx.get_stream().get_miopen(),
-                                      &alpha,
-                                      x_desc.get(),
-                                      args[0].implicit(),
-                                      w_desc.get(),
-                                      args[1].implicit(),
-                                      cd.get(),
-                                      algo,
-                                      &beta,
-                                      y_desc.get(),
-                                      args[3].implicit(),
-                                      args[2].implicit(),
-                                      args[2].get_shape().bytes());
+                                           &alpha,
+                                           x_desc.get(),
+                                           args[0].implicit(),
+                                           w_desc.get(),
+                                           args[1].implicit(),
+                                           cd.get(),
+                                           algo,
+                                           &beta,
+                                           y_desc.get(),
+                                           args[3].implicit(),
+                                           args[2].implicit(),
+                                           args[2].get_shape().bytes());
     if(status != miopenStatusSuccess)
     {
         MIGRAPHX_THROW("QUANT_CONVOLUTION: run convolution forward failed");
@@ -62,10 +62,10 @@ shape miopen_quant_convolution::compile(context& ctx,
                                              &workspace_size);
     workspace_shape = shape{shape::int8_type, {workspace_size}};
 
-    auto arg_vec4_x     = to_gpu(generate_argument(pack_int8_shape(inputs[0])));
-    auto arg_vec4_w     = to_gpu(generate_argument(pack_int8_shape(inputs[1])));
-    auto y         = allocate_gpu(output_shape);
-    auto workspace = allocate_gpu(workspace_shape);
+    auto arg_vec4_x = to_gpu(generate_argument(pack_int8_shape(inputs[0])));
+    auto arg_vec4_w = to_gpu(generate_argument(pack_int8_shape(inputs[1])));
+    auto y          = allocate_gpu(output_shape);
+    auto workspace  = allocate_gpu(workspace_shape);
 
     int algo_count = 1;
     miopenConvAlgoPerf_t perf;

src/targets/gpu/quant_gemm.cpp

@@ -61,8 +61,8 @@ shape rocblas_quant_gemm::compute_shape(const std::vector<shape>& inputs) const
 }
 
 argument rocblas_quant_gemm::compute(context& ctx,
-                                    const shape& output_shape,
-                                    const std::vector<argument>& args) const
+                                     const shape& output_shape,
+                                     const std::vector<argument>& args) const
 {
     bool transa     = args[0].get_shape().transposed();
     bool transb     = args[1].get_shape().transposed();