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* Qnn Dense layer. * Reformatting code. * Reformatting code and making the test case more readable. * Fixing lint issues. * Fixing test method names to pass the nose related configurations. * Aligning the code for code style.
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| /* | ||
| * 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. | ||
| */ | ||
|
|
||
| /*! | ||
| * Copyright (c) 2019 by Contributors | ||
| * \file src/relay/qnn/op/dense.cc | ||
| * \brief Property def of qnn dense operator. | ||
| */ | ||
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| #include <tvm/relay/base.h> | ||
| #include <tvm/relay/op.h> | ||
| #include <tvm/relay/op_attr_types.h> | ||
| #include <tvm/relay/qnn/attrs.h> | ||
| #include "../../op/nn/nn.h" | ||
| #include "../../pass/pattern_util.h" | ||
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| namespace tvm { | ||
| namespace relay { | ||
| namespace qnn { | ||
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| // relay.op.qnn.dense | ||
| TVM_REGISTER_NODE_TYPE(QnnDenseAttrs); | ||
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| bool QnnDenseRel(const Array<Type>& types, | ||
| int num_inputs, | ||
| const Attrs& attrs, | ||
| const TypeReporter& reporter) { | ||
| CHECK_EQ(types.size(), 3); | ||
| const auto* data = types[0].as<TensorTypeNode>(); | ||
| const auto* weight = types[1].as<TensorTypeNode>(); | ||
| if (data == nullptr || weight == nullptr) return false; | ||
| const auto* param = attrs.as<QnnDenseAttrs>(); | ||
| CHECK(param != nullptr) << "QnnConv2DAttrs cannot be nullptr."; | ||
| CHECK(data->dtype == Int(8) || data->dtype == UInt(8)) | ||
| << "Expected quantized dense type(int8, uint8) for input but was " << data->dtype; | ||
| CHECK(weight->dtype == Int(8) || weight->dtype == UInt(8)) | ||
| << "Expected quantized dense type(int8, uint8) for weight but was " << weight->dtype; | ||
| CHECK(param->out_dtype == Int(32)) | ||
| << "Expected quantized dense type(int32) for output but was " << param->out_dtype; | ||
| CHECK(param->out_dtype.bits() > 0) << "Output dtype bits should be greater than 0."; | ||
| return DenseRel<QnnDenseAttrs>(types, num_inputs, attrs, reporter); | ||
| } | ||
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| // Positional relay function to create quantized dense operator used by frontend FFI. | ||
| Expr MakeQuantizedDense(Expr data, | ||
| Expr weight, | ||
| IndexExpr units, | ||
| int32_t input_zero_point, | ||
| int32_t kernel_zero_point, | ||
| DataType out_dtype) { | ||
| auto attrs = make_node<QnnDenseAttrs>(); | ||
| attrs->units = std::move(units); | ||
| attrs->out_dtype = out_dtype; | ||
| attrs->input_zero_point = input_zero_point; | ||
| attrs->kernel_zero_point = kernel_zero_point; | ||
| static const Op& op = Op::Get("qnn.dense"); | ||
| return CallNode::make(op, {data, weight}, Attrs(attrs), {}); | ||
| } | ||
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| /** | ||
| * \brief Lowers Qnn convolution in terms of core operators in relay. | ||
| * Mathematically it is equals to - | ||
| * Dense((quantized_input - input_zero_point;int32), (quantized_kernel - kernel_zero_point; int32)) | ||
| * | ||
| * \param attrs QnnDenseAttrs for Qnn Dense layer. | ||
| * \param new_args The new mutated args to the call node. | ||
| * \param arg_types The data types of input and output. | ||
| * \reutrn The sequence of Relay ops for qnn cov2d op. | ||
| */ | ||
| Expr QnnDenseCanonicalize(const Attrs& attrs, | ||
| const Array<Expr>& new_args, | ||
| const Array<tvm::relay::Type>& arg_types) { | ||
| CHECK_EQ(new_args.size(), 2); | ||
| Expr quantized_data = new_args[0]; | ||
| Expr quantized_kernel = new_args[1]; | ||
| const auto* qnn_dense_attrs = attrs.as<QnnDenseAttrs>(); | ||
| Expr quantized_data_int32 = Cast(quantized_data, Int(32)); | ||
| if (qnn_dense_attrs->input_zero_point != 0) { | ||
| quantized_data_int32 = Subtract(quantized_data_int32, | ||
| MakeConstantScalar(Int(32), | ||
| qnn_dense_attrs->input_zero_point)); | ||
| } | ||
| Expr quantized_kernel_int32 = Cast(quantized_kernel, Int(32)); | ||
| if (qnn_dense_attrs->kernel_zero_point != 0) { | ||
| quantized_kernel_int32 = Subtract(quantized_kernel_int32, | ||
| MakeConstantScalar(Int(32), | ||
| qnn_dense_attrs->kernel_zero_point)); | ||
| } | ||
| Expr int32_dense = Dense(quantized_data_int32, | ||
| quantized_kernel_int32, | ||
| qnn_dense_attrs->units, | ||
| qnn_dense_attrs->out_dtype); | ||
| return int32_dense; | ||
| } | ||
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| RELAY_REGISTER_OP("qnn.dense") | ||
| .describe(R"code(Applies a linear transformation: :math:`Y = XW^T`. | ||
| - **data**: quantized(int8, unit8) `(x1, x2, ..., xn, input_dim)` | ||
| - **weight**: quantized(int8, unit8) `(units, input_dim)` | ||
| - **out**: quantized(int32) `(x1, x2, ..., xn, units)`. | ||
| )code" TVM_ADD_FILELINE) | ||
| .set_attrs_type_key("relay.attrs.qnn.QnnDenseAttrs") | ||
| .set_num_inputs(2) | ||
| .add_argument("data", "quantized nD Tensor", "Input data.") | ||
| .add_argument("weight", "quantized 2D Tensor", "Weight matrix.") | ||
| .set_support_level(11) | ||
| .add_type_rel("QDense", DenseRel<QnnDenseAttrs>) | ||
| .set_attr<FTVMLegalize>("FTVMQnnCanonicalize", QnnDenseCanonicalize); | ||
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| TVM_REGISTER_API("relay.qnn.op._make.dense") | ||
| .set_body_typed(MakeQuantizedDense); | ||
|
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| } // namespace qnn | ||
| } // namespace relay | ||
| } // namespace tvm |
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