// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // 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 "lite/operators/reshape_op.h" #include "lite/kernels/npu/bridges/registry.h" #include "lite/kernels/xpu/bridges/graph.h" #include "lite/kernels/xpu/bridges/utility.h" namespace paddle { namespace lite { namespace subgraph { namespace xpu { int ReshapeConverter(void* ctx, OpLite* op, KernelBase* kernel) { CHECK(ctx != nullptr); CHECK(op != nullptr); auto graph = static_cast(ctx); auto op_info = op->op_info(); auto scope = op->scope(); auto op_type = op_info->Type(); VLOG(3) << "[XPU] Converting " + op_type + "..."; // Get input and output vars and op attributes auto x_name = op_info->Input("X").front(); auto x = scope->FindMutableTensor(x_name); auto x_dims = x->dims(); auto out_name = op_info->Output("Out").front(); // X node std::shared_ptr x_node = nullptr; if (graph->Has(x_name)) { x_node = graph->Get(x_name); } else { x_node = graph->Add(x_name, *x); } std::vector shape; if (HasInputArg(op_info, scope, "ShapeTensor")) { auto shape_tensor_names = op_info->Input("ShapeTensor"); for (auto shape_tensor_name : shape_tensor_names) { auto shape_tensor = scope->FindMutableTensor(shape_tensor_name); CHECK(shape_tensor->persistable()); auto shape_tensor_data = shape_tensor->mutable_data(); shape.emplace_back(shape_tensor_data[0]); } CHECK_GT(shape.size(), 0) << "[XPU] ShapeError: When `shape` in ReshapeOp is a list or tuple " "which contains Tensor, the shape's size can't be zero. " "But received shape's size is " << shape.size(); } else if (HasInputArg(op_info, scope, "Shape")) { auto actual_shape_name = op_info->Input("Shape").front(); auto actual_shape = scope->FindMutableTensor(actual_shape_name); CHECK(actual_shape->persistable()); auto actual_shape_dims = actual_shape->dims(); auto actual_shape_data = actual_shape->mutable_data(); auto shape = std::vector( actual_shape_data, actual_shape_data + actual_shape_dims.production()); } else if (op_info->HasAttr("shape")) { shape = op_info->GetAttr>("shape"); } else { LOG(WARNING) << "[XPU] No new shape for reshape op"; return FAILED; } auto out_dims = operators::ValidateShape(shape, x_dims); // Reshape node graph->Add(out_name, graph->builder_.CreateReshape(*x_node->data(), CvtShape(out_dims)), x_node->precision(), x_node->layout()); return REBUILD_WHEN_SHAPE_CHANGED; } } // namespace xpu } // namespace subgraph } // namespace lite } // namespace paddle REGISTER_SUBGRAPH_BRIDGE(reshape2, kXPU, paddle::lite::subgraph::xpu::ReshapeConverter); REGISTER_SUBGRAPH_BRIDGE(reshape, kXPU, paddle::lite::subgraph::xpu::ReshapeConverter);