| #include "bound_shape_inferencer.h" |
| #include "caffe2/core/operator_schema.h" |
| #include "caffe2/core/tensor_impl.h" |
| #include "caffe2/utils/proto_utils.h" |
| #include "caffe2/utils/string_utils.h" |
| |
| namespace caffe2 { |
| |
| namespace { |
| std::vector<int64_t> ConvertToVec( |
| const ::google::protobuf::RepeatedField<::google::protobuf::int64>& in) { |
| std::vector<int64_t> out; |
| out.reserve(in.size()); |
| for (const auto d : in) { |
| out.push_back(d); |
| } |
| return out; |
| } |
| |
| int64_t SizeFromDim(const TensorShape& shape, int axis) { |
| int64_t r = 1; |
| for (int i = axis; i < shape.dims_size(); ++i) { |
| r *= shape.dims(i); |
| } |
| return r; |
| } |
| |
| int64_t SizeToDim(const TensorShape& shape, int axis) { |
| CAFFE_ENFORCE_LE(axis, shape.dims_size()); |
| int64_t r = 1; |
| for (int i = 0; i < axis; ++i) { |
| r *= shape.dims(i); |
| } |
| return r; |
| } |
| |
| void EnsureShapeNames(std::unordered_map<std::string, ShapeInfo>* info) { |
| for (auto& kv : *info) { |
| kv.second.shape.set_name(kv.first); |
| } |
| } |
| } // namespace |
| |
| void BoundShapeInferencer::InferBoundShapeAndType( |
| const NetDef& net, |
| const std::unordered_map<std::string, ShapeInfo>& info) { |
| const static std::unordered_set<std::string> unsupported{"Tile"}; |
| shape_info_ = info; |
| |
| for (const auto& op : net.op()) { |
| VLOG(1) << op.type(); |
| if (unsupported.count(op.type())) { |
| continue; |
| } |
| |
| if (op.type() == "SparseLengthsSum" || |
| op.type() == "SparseLengthsSumFused8BitRowwise" || |
| op.type() == "SparseLengthsWeightedSum" || |
| op.type() == "SparseLengthsWeightedSumFused8BitRowwise") { |
| InferSparseLengthsSum(op); |
| } else if (op.type() == "FC" || op.type() == "FCTransposed") { |
| InferFC(op); |
| } else if (op.type() == "Concat") { |
| InferConcat(op); |
| } else if (op.type() == "Reshape") { |
| InferReshape(op); |
| } else if (op.type() == "LengthsRangeFill") { |
| InferLengthsRangeFill(op); |
| } else if ( |
| (caffe2::StartsWith(op.type(), "GivenTensor") && |
| caffe2::EndsWith(op.type(), "Fill")) || |
| op.type() == "ConstantFill" || op.type() == "Int8GivenTensorFill" || |
| op.type() == "Int8GivenIntTensorFill") { |
| InferGivenTensorFill(op); |
| } else if (op.type() == "Shape") { |
| InferShape(op); |
| } else { |
| InferCommonOp(op); |
| } |
| } |
| |
| // Doing a reverse pass to infer the input shapes if applicable |
| for (int i = net.op_size() - 1; i >= 0; --i) { |
| const auto& op = net.op(i); |
| if (op.type() == "Concat") { |
| InferConcatInputs(op); |
| } |
| } |
| |
| // Make sure shape has name |
| EnsureShapeNames(&shape_info_); |
| } |
| |
| TensorShape& BoundShapeInferencer::CheckAndSetTensorShapeAndType( |
| const std::string& name, |
| ShapeInfo::DimType t, |
| std::vector<int64_t> bound_dims, |
| TensorProto::DataType type, |
| bool is_quantized) { |
| auto rt = shape_info_.emplace(name, ShapeInfo()); |
| ShapeInfo& shape_info = rt.first->second; |
| TensorShape& shape = shape_info.shape; |
| if (is_quantized) { |
| shape_info.is_quantized = true; |
| shape_info.q_info.scale = 1; |
| shape_info.q_info.offset = 0; |
| } |
| if (!rt.second) { |
| // Check shape consistency |
| CAFFE_ENFORCE_EQ(shape.dims_size(), bound_dims.size()); |
| // For shapes that was provided as a hint at the input of the net, fix the |
| // batch size first. |
| if (shape.dims_size() > 0 && |
| shape_info.dim_type == ShapeInfo::DimType::UNKNOWN && |
| t > ShapeInfo::DimType::CONSTANT) { |
| shape_info.dim_type = t; |
| shape.set_dims(0, bound_dims.front()); |
| } |
| for (int i = 0; i < shape.dims_size(); ++i) { |
| CAFFE_ENFORCE_EQ( |
| shape.dims(i), |
| bound_dims[i], |
| "Shape inconsistency found in tensor ", |
| name, |
| " on dim ", |
| i, |
| " (", |
| shape.dims(i), |
| " vs ", |
| bound_dims[i], |
| ")"); |
| } |
| return shape; |
| } |
| |
| shape_info.dim_type = t; |
| shape.mutable_dims()->Clear(); |
| for (const auto d : bound_dims) { |
| shape.add_dims(d); |
| } |
| shape.set_data_type(type); |
| return shape; |
| } |
| |
| std::vector<TensorShape> InferOutput( |
| const OperatorDef& op, |
| const std::vector<TensorShape>& input_shapes) { |
| const OpSchema* schema = OpSchemaRegistry::Schema(op.type()); |
| CAFFE_ENFORCE(schema); |
| return schema->InferTensor(op, input_shapes); |
| } |
| |
| void BoundShapeInferencer::InferGivenTensorFill(const OperatorDef& op) { |
| CAFFE_ENFORCE_EQ(op.output_size(), 1, op.type(), " must have 1 output"); |
| InferCommonOp(op); |
| auto it = shape_info_.find(op.output(0)); |
| if (it != shape_info_.end()) { |
| it->second.dim_type = ShapeInfo::DimType::CONSTANT; |
| } |
| } |
| |
| void BoundShapeInferencer::InferLengthsRangeFill(const OperatorDef& op) { |
| CAFFE_ENFORCE_EQ(op.input_size(), 1, "LengthsRangeFill must have 1 input"); |
| CAFFE_ENFORCE_EQ(op.output_size(), 1, "LengthsRangeFill must have 1 output"); |
| // Both input and ouptut of LengthsRangeFill is int32: |
| // https://fburl.com/fhwb5666 |
| CheckAndSetTensorShapeAndType( |
| op.input(0), |
| ShapeInfo::DimType::BATCH, |
| {spec_.max_batch_size}, |
| TensorProto_DataType_INT32, |
| false); |
| CheckAndSetTensorShapeAndType( |
| op.output(0), |
| ShapeInfo::DimType::SEQ, |
| {spec_.max_seq_size}, |
| TensorProto_DataType_INT32, |
| false); |
| current_dim_type_ = ShapeInfo::DimType::SEQ; |
| } |
| |
| void BoundShapeInferencer::InferSparseLengthsSum(const OperatorDef& op) { |
| CAFFE_ENFORCE_GE( |
| op.input_size(), 3, op.type(), " must have at least 3 inputs"); |
| const auto it = shape_info_.find(op.input(0)); |
| CAFFE_ENFORCE( |
| it != shape_info_.end(), |
| "Shape of DATA input of SparseLengthsSum ", |
| op.input(0), |
| " needs to be presented"); |
| CAFFE_ENFORCE_EQ( |
| it->second.shape.dims().size(), |
| 2, |
| "DATA input ", |
| op.input(0), |
| "needs to be 2D"); |
| |
| int weight = (op.type() == "SparseLengthsWeightedSum" || |
| op.type() == "SparseLengthsWeightedSumFused8BitRowwise") |
| ? 1 |
| : 0; |
| |
| if (weight) { |
| CAFFE_ENFORCE_EQ( |
| op.input_size(), 4, "SparseLengthsWeightedSum must have 4 inputs"); |
| CheckAndSetTensorShapeAndType( |
| op.input(weight), |
| ShapeInfo::DimType::SEQ, |
| {spec_.max_seq_size}, |
| TensorProto_DataType_FLOAT, |
| false); |
| } |
| |
| // Bound inputs |
| CheckAndSetTensorShapeAndType( |
| op.input(1 + weight), |
| ShapeInfo::DimType::SEQ, |
| {spec_.max_seq_size}, |
| TensorProto_DataType_INT64, |
| false); |
| CheckAndSetTensorShapeAndType( |
| op.input(2 + weight), |
| ShapeInfo::DimType::BATCH, |
| {spec_.max_batch_size}, |
| TensorProto_DataType_INT32, |
| false); |
| |
| // Infer output |
| CAFFE_ENFORCE_EQ(it->second.shape.dims_size(), 2); |
| current_dim_type_ = ShapeInfo::DimType::BATCH; |
| current_max_batch_size_ = spec_.max_batch_size; |
| auto output_dim1 = it->second.shape.dims(1); |
| // If the op is SparseLengthsSumFused8BitRowwise, we need to extract 4 for |
| // scale and 4 byte for bias (https://fburl.com/t6dp9tsc) |
| if (op.type() == "SparseLengthsSumFused8BitRowwise" || |
| op.type() == "SparseLengthsWeightedSumFused8BitRowwise") { |
| output_dim1 -= 8; |
| } |
| CheckAndSetTensorShapeAndType( |
| op.output(0), |
| ShapeInfo::DimType::BATCH, |
| {spec_.max_batch_size, output_dim1}, |
| TensorProto_DataType_FLOAT, |
| false); |
| } |
| |
| void BoundShapeInferencer::InferShape(const OperatorDef& op) { |
| InferCommonOp(op); |
| // old_shape should be a constant |
| if (op.output_size() > 0 && shape_info_.count(op.output(0))) { |
| shape_info_[op.output(0)].dim_type = ShapeInfo::DimType::CONSTANT; |
| } |
| } |
| |
| void BoundShapeInferencer::InferReshape(const OperatorDef& op) { |
| InferCommonOp(op); |
| // old_shape should be a constant |
| if (op.output_size() > 1 && shape_info_.count(op.output(1))) { |
| shape_info_[op.output(1)].dim_type = ShapeInfo::DimType::CONSTANT; |
| } |
| } |
| |
| void BoundShapeInferencer::InferConcatInputs(const OperatorDef& op) { |
| ArgumentHelper helper(op); |
| const auto add_axis = helper.GetSingleArgument<int32_t>("add_axis", 0); |
| if (add_axis) { |
| return; |
| } else if (op.output_size() == 0 || !shape_info_.count(op.output(0))) { |
| return; |
| } |
| |
| const auto axis = helper.HasArgument("axis") |
| ? helper.GetSingleArgument<int32_t>("axis", -1) |
| : GetDimFromOrderString( |
| helper.GetSingleArgument<string>("order", "NCHW")); |
| |
| const auto& shape_info = shape_info_.at(op.output(0)); |
| int output_channel = shape_info.shape.dims(axis); |
| int missing_shape_infos = 0; |
| int channel_acc = 0; |
| std::string input_to_infer; |
| for (const auto& i : op.input()) { |
| const auto it = shape_info_.find(i); |
| if (it != shape_info_.end()) { |
| const auto& current_input_shape = it->second; |
| channel_acc += current_input_shape.shape.dims(axis); |
| } else if (missing_shape_infos) { |
| LOG(INFO) << "More than one missing shapes, previous one: " |
| << input_to_infer; |
| // We can only infer one missing input shape info |
| return; |
| } else { |
| ++missing_shape_infos; |
| input_to_infer = i; |
| } |
| } |
| |
| if (missing_shape_infos && !input_to_infer.empty()) { |
| auto input_shape_info = shape_info; |
| input_shape_info.shape.set_dims(axis, output_channel - channel_acc); |
| shape_info_.emplace(input_to_infer, std::move(input_shape_info)); |
| |
| // Infer the shape of the second output of Concat |
| InferCommonOp(op); |
| if (op.output_size() > 1 && shape_info_.count(op.output(1))) { |
| shape_info_[op.output(1)].dim_type = ShapeInfo::DimType::CONSTANT; |
| } |
| } |
| } |
| |
| // For concat net, if some inputs are missing and we have add_axis argument, it |
| // means that all the inputs should be of the same dimension. In this case, we |
| // can infer the shape of the missing inputs |
| void BoundShapeInferencer::InferConcat(const OperatorDef& op) { |
| ArgumentHelper helper(op); |
| auto add_axis = helper.GetSingleArgument<int32_t>("add_axis", 0); |
| if (add_axis) { |
| ShapeInfo* ref_input_shape = nullptr; |
| std::string ref_name; |
| std::unordered_set<std::string> missing_shape_inputs; |
| for (const auto& i : op.input()) { |
| const auto it = shape_info_.find(i); |
| if (it != shape_info_.end()) { |
| const auto& current_input_shape = it->second; |
| if (ref_input_shape) { |
| CAFFE_ENFORCE_EQ( |
| ref_input_shape->shape.dims_size(), |
| current_input_shape.shape.dims_size(), |
| ref_name, |
| " vs ", |
| i); |
| for (int j = 0; j < ref_input_shape->shape.dims_size(); ++j) { |
| CAFFE_ENFORCE_EQ( |
| ref_input_shape->shape.dims(j), |
| current_input_shape.shape.dims(j), |
| "Mismatched size on dim ", |
| j, |
| " between ", |
| ref_name, |
| " and ", |
| i, |
| " (", |
| ref_input_shape->shape.dims(j), |
| " vs ", |
| current_input_shape.shape.dims(j), |
| ")"); |
| } |
| } else { |
| ref_input_shape = &it->second; |
| ref_name = i; |
| } |
| } else { |
| missing_shape_inputs.emplace(i); |
| } |
| } |
| |
| if (ref_input_shape) { |
| current_dim_type_ = ref_input_shape->dim_type; |
| for (const auto& i : missing_shape_inputs) { |
| shape_info_.emplace(i, *ref_input_shape); |
| } |
| } |
| } |
| InferCommonOp(op); |
| // split_info should be a constant |
| if (op.output_size() > 1 && shape_info_.count(op.output(1))) { |
| shape_info_[op.output(1)].dim_type = ShapeInfo::DimType::CONSTANT; |
| } |
| } |
| |
| void BoundShapeInferencer::InferFC(const OperatorDef& op) { |
| CAFFE_ENFORCE_EQ(op.input_size(), 3, "FC has to have 3 inputs"); |
| const auto w_it = shape_info_.find(op.input(1)); |
| CAFFE_ENFORCE( |
| w_it != shape_info_.end(), |
| "Shape of WEIGHT input of FC ", |
| op.input(1), |
| " needs to be presented"); |
| const ShapeInfo& w_shape_info = w_it->second; |
| const auto b_it = shape_info_.find(op.input(2)); |
| CAFFE_ENFORCE( |
| w_it != shape_info_.end(), |
| "Shape of BIAS input of FC ", |
| op.input(2), |
| " needs to be presented"); |
| const ShapeInfo& b_shape_info = b_it->second; |
| auto x_it = shape_info_.find(op.input(0)); |
| if (x_it == shape_info_.end()) { |
| // We don't have a hint at the x input we try to deduce it from weight shape |
| ArgumentHelper helper(op); |
| auto axis = helper.GetSingleArgument<int32_t>("axis", 1); |
| auto axis_w = helper.GetSingleArgument<int32_t>("axis_w", 1); |
| const TensorShape w_shape = w_shape_info.shape; |
| bool transposed = (op.type() == "FC") ? false : true; |
| const int canonical_axis_w = |
| canonical_axis_index_(axis_w, w_shape.dims().size()); |
| const int64_t K = transposed ? SizeToDim(w_shape, canonical_axis_w) |
| : SizeFromDim(w_shape, canonical_axis_w); |
| std::vector<int64_t> dims; |
| for (int i = 0; i < axis - 1; ++i) { |
| dims.push_back(1); |
| } |
| dims.push_back(spec_.max_batch_size); |
| dims.push_back(K); |
| current_dim_type_ = ShapeInfo::DimType::BATCH; |
| current_max_batch_size_ = spec_.max_batch_size; |
| CheckAndSetTensorShapeAndType( |
| op.input(0), |
| ShapeInfo::DimType::BATCH, |
| dims, |
| w_shape.data_type(), |
| false); |
| } else { |
| ShapeInfo& x_shape_info = x_it->second; |
| if (x_shape_info.dim_type != ShapeInfo::DimType::BATCH) { |
| CAFFE_ENFORCE_GE(x_shape_info.shape.dims_size(), 1); |
| x_shape_info.shape.set_dims(0, spec_.max_batch_size); |
| x_shape_info.dim_type = ShapeInfo::DimType::BATCH; |
| } |
| } |
| |
| // Standard shape inference for outputs |
| std::vector<TensorShape> input_shapes{ |
| shape_info_[op.input(0)].shape, w_shape_info.shape, b_shape_info.shape}; |
| std::vector<TensorShape> output_shapes = InferOutput(op, input_shapes); |
| CAFFE_ENFORCE_EQ(output_shapes.size(), 1); |
| CheckAndSetTensorShapeAndType( |
| op.output(0), |
| ShapeInfo::DimType::BATCH, |
| ConvertToVec(output_shapes[0].dims()), |
| output_shapes[0].data_type(), |
| false); |
| } |
| |
| void BoundShapeInferencer::InferCommonOp(const OperatorDef& op) { |
| // First, we need to check that all the input shape/types are already |
| // presented |
| try { |
| std::vector<TensorShape> input_shapes; |
| for (const auto& input : op.input()) { |
| const auto it = shape_info_.find(input); |
| if (it == shape_info_.end()) { |
| LOG(WARNING) << "Cannot find shape info for " << input << ". Skipping " |
| << op.type(); |
| return; |
| } |
| input_shapes.emplace_back(it->second.shape); |
| } |
| |
| const OpSchema* schema = OpSchemaRegistry::Schema(op.type()); |
| CAFFE_ENFORCE(schema); |
| std::vector<TensorShape> output_shapes; |
| output_shapes = schema->InferTensor(op, input_shapes); |
| int i = 0; |
| bool is_quantized = |
| !(op.type().compare(0, 4, "Int8")) && (op.type() != "Int8Dequantize"); |
| TensorProto::DataType infered_data_type = TensorProto::UNDEFINED; |
| if (is_quantized) { |
| const static std::map<std::string, int> type_info_from_input = { |
| {"Int8Quantize", -1}, // Force this op's output to be uint8 |
| {"Int8ConvRelu", 1}, |
| {"Int8MaxPool", 0}, |
| {"Int8AveragePool", 0}, |
| {"Int8FC", 1}, |
| {"Int8Conv", 1}, |
| {"Int8SumRelu", 0}}; |
| CAFFE_ENFORCE( |
| type_info_from_input.find(op.type()) != type_info_from_input.end(), |
| "Undefined quantized output data type, add it into type_info_from_input"); |
| int target = type_info_from_input.find(op.type())->second; |
| if (target == -1) { |
| infered_data_type = TensorProto::UINT8; |
| } else { |
| CAFFE_ENFORCE(target < input_shapes.size()); |
| infered_data_type = input_shapes[target].data_type(); |
| } |
| } else if (op.type() == "Int8Dequantize") { |
| infered_data_type = TensorProto::FLOAT; |
| } |
| |
| for (const auto& shape : output_shapes) { |
| if (infered_data_type == TensorProto::UNDEFINED) { |
| infered_data_type = shape.data_type(); |
| } |
| if (shape.unknown_shape()) { |
| ++i; |
| continue; |
| } |
| CheckAndSetTensorShapeAndType( |
| op.output(i++), |
| current_dim_type_, |
| ConvertToVec(shape.dims()), |
| infered_data_type, |
| is_quantized); |
| } |
| } catch (const caffe2::EnforceNotMet& e) { |
| LOG(ERROR) << "Enforce not met while inferring shapes for " << op.type() |
| << ": " << e.msg(); |
| } catch (const std::exception& e) { |
| LOG(WARNING) << "Caught exception while inferring shapes for " << op.type() |
| << ": " << e.what(); |
| } |
| } |
| |
| } // namespace caffe2 |
