// 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/graph.h" #include "lite/kernels/npu/bridges/registry.h" #include "lite/kernels/npu/bridges/utility.h" namespace paddle { namespace lite { namespace subgraph { namespace npu { 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 op_type = op_info->Type(); auto scope = op->scope(); VLOG(3) << "[NPU] Converting " + op_type + "..."; // Get input and output vars and op attributes auto x_name = op_info->Input("X").front(); auto x_type = kernel->GetInputDeclType("X"); auto x = scope->FindMutableTensor(x_name); auto x_dims = x->dims(); auto out_name = op_info->Output("Out").front(); auto out_type = kernel->GetOutputDeclType("Out"); // 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); } // Reshape node auto reshape_node = graph->Add( out_name, x_node->precision(), x_node->layout()); auto reshape_op = reshape_node->data(); reshape_op->set_input_tensor(*x_node->data()); // Read shape from "ShapeTensor"(input), or "Shape"(input), or "shape"(attr) if (HasInputArg(op_info, scope, "ShapeTensor")) { LOG(WARNING) << "[NPU] not support \"Shape\" from more than one Tensor."; return FAILED; } else if (HasInputArg(op_info, scope, "Shape")) { auto actual_shape_name = op_info->Input("Shape").front(); // auto actual_shape_type = kernel->GetInputDeclType("Shape"); // CHECK(actual_shape_type->precision() == PRECISION(kInt32)); // CHECK(actual_shape_type->layout() == DATALAYOUT(kNCHW)); std::shared_ptr actual_shape_node = nullptr; if (graph->Has(actual_shape_name)) { actual_shape_node = graph->Get(actual_shape_name); } else { auto actual_shape = scope->FindMutableTensor(actual_shape_name); 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()); auto out_shape = lite::operators::ValidateShape(shape, x_dims); if (out_shape.size() > 4) { LOG(WARNING) << "[NPU] HiAI DDK only supports less than 4 dimensions, " "but Shape has " << out_shape.size(); return FAILED; } actual_shape_node = graph->Add(actual_shape_name, std::vector(out_shape.begin(), out_shape.end())); } reshape_op->set_input_w(*actual_shape_node->data()); } else { auto shape = op_info->GetAttr>("shape"); auto out_shape = lite::operators::ValidateShape(shape, x_dims); if (out_shape.size() > 4) { LOG(WARNING) << "[NPU] HiAI DDK only supports less than 4 dimensions, " "but shape has " << out_shape.size(); return FAILED; } reshape_op->set_attr_shape( ge::AttrValue::LIST_INT(out_shape.begin(), out_shape.end())); } return REBUILD_WHEN_SHAPE_CHANGED; } } // namespace npu } // namespace subgraph } // namespace lite } // namespace paddle REGISTER_SUBGRAPH_BRIDGE(reshape, kNPU, paddle::lite::subgraph::npu::ReshapeConverter); REGISTER_SUBGRAPH_BRIDGE(reshape2, kNPU, paddle::lite::subgraph::npu::ReshapeConverter);