add support for 3 dim inputs of mlu subgraph op

lite/core/mir/mlu_postprocess_pass.cc

@@ -50,10 +50,9 @@ Node* MLUPostprocessPass::InsertCastBefore(const std::string& op_type,
     op_desc.SetAttr<int>("out_dtype", 4);  // FP16
     op_desc.SetInput("X", {cur_node->AsArg().name});
     op_desc.SetOutput("Out", {cast_arg_name});
-  } else if (op_type == "transpose") {
+  } else if (op_type == "layout") {
     // NCHW -> NHWC
-    op_desc.SetAttr<std::vector<int>>("axis", {0, 2, 3, 1});
-    op_desc.SetInput("X", {cur_node->AsArg().name});
+    op_desc.SetInput("Input", {cur_node->AsArg().name});
     op_desc.SetOutput("Out", {cast_arg_name});
   } else if (op_type == "io_copy") {
     op_desc.SetInput("Input", {cur_node->AsArg().name});
@@ -72,8 +71,13 @@ Node* MLUPostprocessPass::InsertCastBefore(const std::string& op_type,
       if (PrecisionCompatibleTo(*in_arg_ty, *cur_node->AsArg().type)) {
         is_found = true;
       }
-    } else if (op_type == "transpose") {
+    } else if (op_type == "layout") {
+      const Type* in_arg_ty = kernel->GetInputDeclType("Input");
+      const Type* out_arg_ty = kernel->GetOutputDeclType("Out");
+      if (DataLayoutCompatible(*in_arg_ty, *cur_node->AsArg().type) &&
+          DataLayoutCompatible(*out_arg_ty, *cast_type)) {
         is_found = true;
+      }
     } else if (op_type == "io_copy") {
       const Type* in_arg_ty = kernel->GetInputDeclType("Input");
       const Type* out_arg_ty = kernel->GetOutputDeclType("Out");
@@ -89,8 +93,13 @@ Node* MLUPostprocessPass::InsertCastBefore(const std::string& op_type,
       // we pick the kernel
       cast_inst->AsStmt(op_type, std::move(selected_kernels), cast_op);
       auto& stmt = cast_inst->AsStmt();
+      if (op_type == "layout") {
         stmt.picked_kernel().SetContext(
-          ContextScheduler::Global().NewContext(stmt.picked_kernel().target()));
+            ContextScheduler::Global().NewContext(TARGET(kX86)));
+      } else {
+        stmt.picked_kernel().SetContext(ContextScheduler::Global().NewContext(
+            stmt.picked_kernel().target()));
+      }
       break;
     }
   }
@@ -127,10 +136,9 @@ Node* MLUPostprocessPass::InsertCastAfter(const std::string& op_type,
     op_desc.SetAttr<int>("out_dtype", 5);  // FP16
     op_desc.SetInput("X", {cast_arg_name});
     op_desc.SetOutput("Out", {cur_node->AsArg().name});
-  } else if (op_type == "transpose") {
+  } else if (op_type == "layout") {
     // NHWC -> NCHW
-    op_desc.SetAttr<std::vector<int>>("axis", {0, 3, 1, 2});
-    op_desc.SetInput("X", {cast_arg_name});
+    op_desc.SetInput("Input", {cast_arg_name});
     op_desc.SetOutput("Out", {cur_node->AsArg().name});
   } else if (op_type == "io_copy") {
     op_desc.SetInput("Input", {cast_arg_name});
@@ -151,8 +159,13 @@ Node* MLUPostprocessPass::InsertCastAfter(const std::string& op_type,
       if (PrecisionCompatibleTo(*in_arg_ty, *cast_type)) {
         is_found = true;
       }
-    } else if (op_type == "transpose") {
+    } else if (op_type == "layout") {
+      const Type* in_arg_ty = kernel->GetInputDeclType("Input");
+      const Type* out_arg_ty = kernel->GetOutputDeclType("Out");
+      if (DataLayoutCompatible(*in_arg_ty, *cast_type) &&
+          DataLayoutCompatible(*out_arg_ty, *cur_node->AsArg().type)) {
         is_found = true;
+      }
     } else if (op_type == "io_copy") {
       const Type* in_arg_ty = kernel->GetInputDeclType("Input");
       const Type* out_arg_ty = kernel->GetOutputDeclType("Out");
@@ -168,8 +181,13 @@ Node* MLUPostprocessPass::InsertCastAfter(const std::string& op_type,
       // we pick the kernel
       cast_inst->AsStmt(op_type, std::move(selected_kernels), cast_op);
       auto& stmt = cast_inst->AsStmt();
+      if (op_type == "layout") {
         stmt.picked_kernel().SetContext(
-          ContextScheduler::Global().NewContext(stmt.picked_kernel().target()));
+            ContextScheduler::Global().NewContext(TARGET(kX86)));
+      } else {
+        stmt.picked_kernel().SetContext(ContextScheduler::Global().NewContext(
+            stmt.picked_kernel().target()));
+      }
       break;
     }
   }
@@ -197,8 +215,8 @@ void MLUPostprocessPass::InsertBefore(SSAGraph* graph,
   // layout cast node
   if (head_type->layout() != inst_type->layout()) {
     cur_node = InsertCastBefore(
-        "transpose",
-        name_prefix + "transpose",
+        "layout",
+        name_prefix + "layout",
         graph,
         cur_node,
         inst_node,
@@ -346,8 +364,8 @@ void MLUPostprocessPass::InsertAfter(SSAGraph* graph,
   // layout cast node
   if (tail_type->layout() != inst_type->layout()) {
     cur_node = InsertCastAfter(
-        "transpose",
-        name_prefix + "transpose",
+        "layout",
+        name_prefix + "layout",
         graph,
         cur_node,
         inst_node,

lite/core/mir/subgraph_cast_display_pass.cc

@@ -53,7 +53,7 @@ class SubgraphCastDisplayPass : public DebugPass {
         for (auto p_in_stmt_node : p_in_arg_node->inlinks) {
           CHECK(p_in_stmt_node->IsStmt());
           std::string stmt_op_type = p_in_stmt_node->AsStmt().op_type();
-          if (stmt_op_type == "cast" || stmt_op_type == "transpose" ||
+          if (stmt_op_type == "cast" || stmt_op_type == "layout" ||
               stmt_op_type == "io_copy") {
             display_debug_info(*p_in_stmt_node, stmt_op_type, true, false);
           } else {
@@ -76,7 +76,7 @@ class SubgraphCastDisplayPass : public DebugPass {
         for (auto p_out_stmt_node : p_out_arg_node->outlinks) {
           CHECK(p_out_stmt_node->IsStmt());
           std::string stmt_op_type = p_out_stmt_node->AsStmt().op_type();
-          if (stmt_op_type == "cast" || stmt_op_type == "transpose" ||
+          if (stmt_op_type == "cast" || stmt_op_type == "layout" ||
               stmt_op_type == "io_copy") {
             display_debug_info(*p_out_stmt_node, stmt_op_type, false, true);
           } else {

lite/core/optimizer.h

@@ -116,12 +116,12 @@ class Optimizer {
            "argument_type_display_pass",
 
            "mlu_subgraph_pass",
-           "mlu_postprocess_pass",
-           // subgraph_cast_display_pass
 
            "runtime_context_assign_pass",
            "argument_type_display_pass",
 
+           "mlu_postprocess_pass",
+
            "memory_optimize_pass"}};
 
       if (passes.size() == 1) {

lite/kernels/mlu/CMakeLists.txt

@@ -6,3 +6,4 @@ add_subdirectory(bridges)
 add_kernel(subgraph_compute_mlu MLU basic SRCS subgraph_compute.cc DEPS ${lite_kernel_deps} ${mlu_subgraph_bridges})
 add_kernel(io_copy_compute_mlu MLU basic SRCS io_copy_compute.cc DEPS ${lite_kernel_deps} ${math_mlu})
 add_kernel(calib_compute_mlu MLU basic SRCS calib_compute.cc DEPS ${lite_kernel_deps} ${math_mlu})
+add_kernel(layout_compute_mlu MLU basic SRCS layout_compute.cc DEPS ${lite_kernel_deps} ${math_mlu})

lite/kernels/mlu/bridges/concat_op.cc

@@ -46,26 +46,37 @@ int ConcatConverter(void* ctx, OpLite* op, KernelBase* kernel) {
     input_dims.push_back(x->dims().Vectorize());
   }
 
-  auto output = scope->FindVar(out_var_name)->GetMutable<Tensor>();
-  int axis = (param_axis < 0) ? (param_axis + output->dims().size()) : param_axis;
+  auto dims = input_dims[0].size();
+  int axis = (param_axis < 0) ? (param_axis + dims) : param_axis;
+  int nhwc_axis = -1;
+  if (dims == 4) {
     int nchw_to_nhwc_axis_map[4] = {0, 3, 1, 2};
-  int nhwc_axis = nchw_to_nhwc_axis_map[axis];
+    nhwc_axis = nchw_to_nhwc_axis_map[axis];
+  } else if (dims == 3) {
+    int nchw_to_nhwc_axis_map[3] = {0, 2, 1};
+    nhwc_axis = nchw_to_nhwc_axis_map[axis];
+  } else {
+    CHECK(0) << "Unsupport dims in mlu concat";
+  }
 
   std::vector<int64_t> output_dims;
-  output_dims.assign(output->dims().size(), 0);
+  output_dims.assign(dims, 0);
 
-  /* std::cout << string_format("concat axis: %d(NCHW), %d(NHWC)", axis, nhwc_axis) << std::endl; */
+  /* std::cout << string_format("concat axis: %d(NCHW), %d(NHWC)", axis,
+   * nhwc_axis) << std::endl; */
 
   for (int i = 0; i < output_dims.size(); ++i) {
     if (i == nhwc_axis) {
-      for (auto &dim : input_dims) output_dims[i] += dim[i];
+      for (auto& dim : input_dims) output_dims[i] += dim[i];
     } else {
       output_dims[i] = input_dims[0][i];
     }
   }
 
-  /* std::cout << string_format("concat output dim: %ld, %ld, %ld, %ld") << std::endl; */
+  /* std::cout << string_format("concat output dim: %ld, %ld, %ld, %ld") <<
+   * std::endl; */
 
+  auto* output = scope->FindVar(out_var_name)->GetMutable<Tensor>();
   output->Resize(output_dims);
   auto output_tensor = graph->AddNode(
       out_var_name, output_dims, CNML_TENSOR, CNML_NHWC, graph->FPType());

lite/kernels/mlu/bridges/transpose_op.cc

@@ -21,18 +21,37 @@ namespace lite {
 namespace subgraph {
 namespace mlu {
 
-std::vector<int> axis_to_4d(std::vector<int> axis) {
-  if (axis.size() >= 4) {
-    return axis;
+std::vector<int> axis_to_nhwc4d(const std::vector<int>& axis) {
+  CHECK_EQ(axis.size(), 4);
+  std::vector<int> new_axis(4, 0);
+  const std::vector<int> axis_map1 = {0, 2, 3, 1};
+  const std::vector<int> axis_map2 = {0, 3, 1, 2};
+  for (size_t i = 0; i < new_axis.size(); ++i) {
+    new_axis[i] = axis_map2[axis[axis_map1[i]]];
   }
-  std::vector<int> new_axis = {0, 1, 2, 3};
-  int i = 0;
-  for (i = 0; i < axis.size(); i++) {
-    new_axis[i] = axis[i];
+  return new_axis;
+}
+
+std::vector<int> axis_to_nhw3d(const std::vector<int>& axis) {
+  CHECK_EQ(axis.size(), 3);
+  std::vector<int> new_axis(3, 0);
+  const std::vector<int> axis_map = {0, 2, 1};
+  for (size_t i = 0; i < new_axis.size(); ++i) {
+    new_axis[i] = axis_map[axis[axis_map[i]]];
   }
+  new_axis.push_back(3);
   return new_axis;
 }
 
+std::vector<int64_t> infer_shape(const std::vector<int64_t>& x_dims,
+                                 const std::vector<int>& axis_nhwc) {
+  std::vector<int64_t> out_dims(x_dims);
+  for (size_t i = 0; i < out_dims.size(); ++i) {
+    out_dims[i] = x_dims[axis_nhwc[i]];
+  }
+  return out_dims;
+}
+
 int TransposeConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   CHECK(ctx != nullptr);
   CHECK(op != nullptr);
@@ -44,17 +63,29 @@ int TransposeConverter(void* ctx, OpLite* op, KernelBase* kernel) {
 
   // Get input vars and op attributes
   auto x_var_name = op_info->Input("X").front();
-  // auto x = scope->FindMutableTensor(x_var_name)->GetMutable<Tensor>();
-  // auto x_dims = x->dims();
+  auto x = scope->FindVar(x_var_name)->GetMutable<Tensor>();
+  auto x_dims = x->dims().Vectorize();
 
   auto out_var_name = op_info->Output("Out").front();
   auto output = scope->FindVar(out_var_name)->GetMutable<Tensor>();
   auto output_dims = output->dims().Vectorize();
 
   auto axis = op_info->GetAttr<std::vector<int>>("axis");
-  auto axis_4d = axis_to_4d(axis);
+
+  std::vector<int> axis_nhwc;
+  if (axis.size() == 4) {
+    axis_nhwc = axis_to_nhwc4d(axis);
+  } else if (axis.size(0 == 3)) {
+    axis_nhwc = axis_to_nhw3d(axis);
+  } else {
+    CHECK(0) << "Unsupport dim in mlu transpose";
+  }
+
+  auto output_dims_nhwc = infer_shape(x_dims, axis_nhwc);
+  output->Resize(output_dims_nhwc);
+
   auto output_tensor = graph->AddNode(
-      out_var_name, output_dims, CNML_TENSOR, CNML_NHWC, graph->FPType());
+      out_var_name, output_dims_nhwc, CNML_TENSOR, CNML_NHWC, graph->FPType());
 
   CHECK(graph->HasNode(x_var_name));
   auto input_tensor = graph->GetNode(x_var_name);
@@ -63,7 +94,7 @@ int TransposeConverter(void* ctx, OpLite* op, KernelBase* kernel) {
   cnmlNdTransposeOpParam_t transpose_param{nullptr};
 
   CNML_CALL(cnmlCreateNdTransposeOpParam(
-      &transpose_param, axis_4d.data(), axis_4d.size()));
+      &transpose_param, axis_nhwc.data(), axis_nhwc.size()));
 
   // Use cnmlCreatexxxOpForward to create op.
   CNML_CALL(cnmlCreateNdTransposeProOp(&transpose_op_,

lite/kernels/mlu/subgraph_compute.h

@@ -97,9 +97,11 @@ class SubgraphEngine : public subgraph::Engine {
     for (auto& inst : origin_program_) {
       auto op = inst.op();
       CHECK(op);
+      std::string op_type = op->op_info()->Type();
       op->CheckShape();
+      if (op_type != "concat") {
         op->InferShape();
-      std::string op_type = op->op_info()->Type();
+      }
       if (!bridges.Exists(op_type, TARGET(kMLU))) {
         LOG(INFO) << "MLU bridges doesn't support op_type: " << op_type;
         return subgraph::FAILED;