mbrookhart commented on a change in pull request #7613: URL: https://github.com/apache/tvm/pull/7613#discussion_r590787205
########## File path: src/relay/qnn/op/simulated_dequantize.cc ########## @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * \file src/relay/qnn/op/simulated_dequantize.cc + * \brief QNN simulated dequantize operator. Mimics the behavior + * of QNN dequantize in floating point with added flexibility. + */ + +#include <tvm/relay/analysis.h> +#include <tvm/relay/op_attr_types.h> +#include <tvm/relay/qnn/attrs.h> + +#include "../../transforms/pattern_utils.h" +#include "../utils.h" + +namespace tvm { +namespace relay { +namespace qnn { + +bool SimulatedDequantizeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, + const TypeReporter& reporter) { + // types = [data_type, datatype_type, scale_type, zp_type, ret_type] + ICHECK_EQ(types.size(), 5); + const auto* data = types[0].as<TensorTypeNode>(); + const auto* dtype = types[1].as<TensorTypeNode>(); + + if ((data == nullptr) || (dtype == nullptr)) { + return false; + } + + // assign output type + reporter->Assign(types[4], TensorType(data->shape, data->dtype)); Review comment: I was thinking just a plain int32 input, not a quantized version. I'm not sure if we'll hit this in real models, but the possibility is always there, and I'd rather not make assumptions about inputs. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
