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@ -18,17 +18,14 @@
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#include <map>
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#include <string>
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#include <vector>
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using anakin::graph::GraphGlobalMem;
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using anakin::AK_FLOAT;
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using anakin::saber::Shape;
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#include "paddle/fluid/inference/anakin/convert/helper.h"
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namespace paddle {
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namespace inference {
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namespace anakin {
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template <typename TargetT>
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void BatchNormOpConverter<TargetT>::operator()(
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template <typename TargetT, ::anakin::Precision PrecisionT>
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void BatchNormOpConverter<TargetT, PrecisionT>::operator()(
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const framework::proto::OpDesc &op, const framework::BlockDesc &block_desc,
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const framework::Scope &scope, bool test_mode) {
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framework::OpDesc op_desc(op, nullptr);
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@ -36,87 +33,46 @@ void BatchNormOpConverter<TargetT>::operator()(
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std::map<std::string, std::string> inputs;
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for (auto k : {"X", "Scale", "Bias", "Mean", "Variance"}) {
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PADDLE_ENFORCE_EQ(op_desc.Input(k).size(), 1UL);
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auto v = op_desc.Input(k).front();
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inputs.insert({k, v});
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}
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auto input = op_desc.Input("X").front();
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auto output = op_desc.Output("Y").front();
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auto op_name = op_desc.Type() + ":" + op_desc.Output("Y").front();
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auto epsilon = boost::get<float>(op_desc.GetAttr("epsilon"));
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// auto momentum = boost::get<float>(op_desc.GetAttr("momentum"));
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auto bn_op_name = op_name + ":bn";
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auto bn_output = bn_op_name + "_output";
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this->engine_->AddOp(bn_op_name, "BatchNorm", {inputs["X"]}, {bn_output});
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this->engine_->AddOp(bn_op_name, "BatchNorm", {input}, {bn_output});
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this->engine_->AddOpAttr(bn_op_name, "epsilon", epsilon);
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this->engine_->AddOpAttr(bn_op_name, "momentum", static_cast<float>(1.0));
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auto scale_op_name = op_name + ":scale";
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auto get_lod_tensor = [this, &scope, &op_name](const std::string &var_name,
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framework::LoDTensor *tensor) {
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auto *v = scope.FindVar(var_name);
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PADDLE_ENFORCE_NOT_NULL(v);
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auto *t = v->GetMutable<framework::LoDTensor>();
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tensor->Resize(t->dims());
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TensorCopySync(*t, platform::CPUPlace(), tensor);
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};
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framework::LoDTensor bias_t;
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framework::LoDTensor mean_t;
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framework::LoDTensor scale_t;
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framework::LoDTensor variance_t;
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get_lod_tensor(inputs["Bias"], &bias_t);
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get_lod_tensor(inputs["Mean"], &mean_t);
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get_lod_tensor(inputs["Scale"], &scale_t);
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get_lod_tensor(inputs["Variance"], &variance_t);
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this->engine_->AddOp(scale_op_name, "Scale", {bn_output}, {output});
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this->engine_->AddOpAttr(scale_op_name, "axis", 1);
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this->engine_->AddOpAttr(scale_op_name, "num_axes", 1);
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this->engine_->AddOpAttr(scale_op_name, "bias_term", true);
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auto fill_shape = [](size_t n, std::vector<int> shape) {
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shape.insert(shape.begin(), 1);
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if (shape.size() < n) {
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shape.insert(shape.end(), n - shape.size(), 1);
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}
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return shape;
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};
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Shape shape1(fill_shape(4, framework::vectorize2int(mean_t.dims())));
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Shape shape2(fill_shape(4, framework::vectorize2int(variance_t.dims())));
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auto *weight1 =
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GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(shape1);
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auto *mean_data = static_cast<float *>(weight1->h_tensor().mutable_data());
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std::copy_n(mean_t.data<float>(), mean_t.numel(), mean_data);
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auto *mean_v = scope.FindVar(op_desc.Input("Mean").front());
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PADDLE_ENFORCE_NOT_NULL(mean_v);
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auto weight1 = pblock_from_var<TargetT>(*mean_v);
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this->engine_->AddOpAttr(bn_op_name, "weight_1", *weight1);
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auto *weight2 =
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GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(shape2);
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auto *variance_data =
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static_cast<float *>(weight2->h_tensor().mutable_data());
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std::copy_n(variance_t.data<float>(), variance_t.numel(), variance_data);
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auto *variance_v = scope.FindVar(op_desc.Input("Variance").front());
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PADDLE_ENFORCE_NOT_NULL(variance_v);
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auto weight2 = pblock_from_var<TargetT>(*variance_v);
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this->engine_->AddOpAttr(bn_op_name, "weight_2", *weight2);
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Shape shape3(std::vector<int>({1, 1, 1, 1}));
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auto *weight3 =
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GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(shape3);
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auto *alpha_data = static_cast<float *>(weight3->h_tensor().mutable_data());
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float weight3_data[] = {1};
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std::copy(std::begin(weight3_data), std::end(weight3_data), alpha_data);
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auto *weight3 = pblock_from_vector<TargetT>(std::vector<float>({1}));
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this->engine_->AddOpAttr(bn_op_name, "weight_3", *weight3);
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Shape scale_shape(fill_shape(4, framework::vectorize2int(scale_t.dims())));
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auto *scale = GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(
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scale_shape);
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auto *scale_data = static_cast<float *>(scale->h_tensor().mutable_data());
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std::copy_n(scale_t.data<float>(), scale_t.numel(), scale_data);
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Shape bias_shape(fill_shape(4, framework::vectorize2int(bias_t.dims())));
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auto *bias = GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(
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bias_shape);
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auto *bias_data = static_cast<float *>(bias->h_tensor().mutable_data());
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std::copy_n(bias_t.data<float>(), bias_t.numel(), bias_data);
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this->engine_->AddOp(scale_op_name, "Scale", {bn_output}, {output});
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this->engine_->AddOpAttr(scale_op_name, "axis", 1);
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this->engine_->AddOpAttr(scale_op_name, "num_axes", 1);
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this->engine_->AddOpAttr(scale_op_name, "bias_term", true);
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auto *scale_v = scope.FindVar(op_desc.Input("Scale").front());
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PADDLE_ENFORCE_NOT_NULL(scale_v);
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auto scale = pblock_from_var<TargetT>(*scale_v);
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this->engine_->AddOpAttr(scale_op_name, "weight_1", *scale);
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auto *bias_v = scope.FindVar(op_desc.Input("Bias").front());
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PADDLE_ENFORCE_NOT_NULL(bias_v);
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auto bias = pblock_from_var<TargetT>(*bias_v);
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this->engine_->AddOpAttr(scale_op_name, "weight_2", *bias);
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}
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@ -125,9 +81,17 @@ void BatchNormOpConverter<TargetT>::operator()(
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} // namespace paddle
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#ifdef PADDLE_WITH_CUDA
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REGISTER_CUDA_ANAKIN_OP_CONVERTER(batch_norm,
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BatchNormOpConverter<::anakin::saber::NV>);
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using bn_nv_fp32 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::NV, ::anakin::Precision::FP32>;
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using bn_nv_int8 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::NV, ::anakin::Precision::INT8>;
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REGISTER_CUDA_ANAKIN_OP_CONVERTER(batch_norm, bn_nv_fp32);
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REGISTER_CUDA_INT8_ANAKIN_OP_CONVERTER(batch_norm, bn_nv_int8);
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#endif
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REGISTER_CPU_ANAKIN_OP_CONVERTER(batch_norm,
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BatchNormOpConverter<::anakin::saber::X86>);
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using bn_cpu_fp32 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::X86, ::anakin::Precision::FP32>;
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using bn_cpu_int8 = ::paddle::inference::anakin::BatchNormOpConverter<
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::anakin::saber::X86, ::anakin::Precision::INT8>;
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REGISTER_CPU_ANAKIN_OP_CONVERTER(batch_norm, bn_cpu_fp32);
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REGISTER_CPU_INT8_ANAKIN_OP_CONVERTER(batch_norm, bn_cpu_int8);
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nhzlx
6 years ago