masahi commented on code in PR #14536: URL: https://github.com/apache/tvm/pull/14536#discussion_r1161388083
########## src/relay/qnn/op/softmax.cc: ########## @@ -0,0 +1,148 @@ +/* + * 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/softmax.cc + * \brief QNN softmax operator. + */ +#include <tvm/relay/analysis.h> +#include <tvm/relay/op_attr_types.h> + +#include "op_common.h" +#include "tvm/ir/expr.h" +#include "tvm/relay/attrs/nn.h" +#include "tvm/relay/type.h" +#include "tvm/runtime/data_type.h" +#include "tvm/runtime/logging.h" +#include "tvm/topi/reduction.h" + +namespace tvm { +namespace relay { +namespace qnn { + +bool QnnSoftmaxRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, + const TypeReporter& reporter) { + // Expected Types: input, scale, zero_point, output_scale, output_zero_point, output + ICHECK_EQ(types.size(), 6); + const auto* x = types[0].as<TensorTypeNode>(); + if (x == nullptr) return false; + ICHECK(x->dtype == DataType::Int(8)) + << "Expected quantized softmax type(int8) for input but was " << x->dtype; + const auto* param = attrs.as<SoftmaxAttrs>(); + ICHECK(param != nullptr) << "SoftmaxAttrs cannot be nullptr."; + + // Check the types of scale and zero points. + for (size_t i = 1; i < 5; ++i) { + if (types[i].as<IncompleteTypeNode>()) { + return false; + } + } + + ICHECK(IsScalarType(types[1], DataType::Float(32))); // scale + ICHECK(IsScalarType(types[2], DataType::Int(32))); // zero_point + ICHECK(IsScalarType(types[3], DataType::Float(32))); // scale + ICHECK(IsScalarType(types[4], DataType::Int(32))); // zero_point + + // Assign types for scale and zero points. + reporter->Assign(types[1], TensorType({}, DataType::Float(32))); // scale + reporter->Assign(types[2], TensorType({}, DataType::Int(32))); // zero_point + reporter->Assign(types[3], TensorType({}, DataType::Float(32))); // scale + reporter->Assign(types[4], TensorType({}, DataType::Int(32))); // zero_point + + // Collect the input tensor and output tensor devoid of scale and zero points to reuse Relay + // IdentityRel infer type function. + Array<Type> tensor_types = {types[0], types[5]}; + return IdentityRel(tensor_types, 2, attrs, reporter); +} + +// Positional relay function to create quantized softmax operator used by frontend FFI. +Expr MakeQuantizedSoftmax(Expr x, int axis, Expr scale, Expr zero_point, Expr output_scale, + Expr output_zero_point) { + auto attrs = make_object<SoftmaxAttrs>(); + attrs->axis = axis; + static const Op& op = Op::Get("qnn.softmax"); + return Call(op, {x, scale, zero_point, output_scale, output_zero_point}, Attrs(attrs), {}); +} + +/* + * \brief Canonicalizes the QNN softmax op. + */ +Expr QnnSoftmaxCanonicalize(const Attrs& attrs, const Array<Expr>& new_args, + const Array<tvm::relay::Type>& arg_types) { + // Expected: input, scale, zero_point, output_scale, output_zero_point + ICHECK_EQ(new_args.size(), 5); + + const Expr input_scale = new_args[1]; + const Expr input_zero_point = new_args[2]; + const Expr output_scale = new_args[3]; + const Expr output_zero_point = new_args[4]; + const int axis = attrs.as<SoftmaxAttrs>()->axis; + + // Refer to the Algorithm 1 in https://arxiv.org/pdf/2207.01405.pdf + + const Expr quantized_data = + Subtract(Cast(new_args[0], DataType::Int(32)), Cast(input_zero_point, DataType::Int(32))); + + const Expr x_0 = ConvertDtype( + Round(Divide(MakeConstantScalar(DataType::Float(32), 1.f), input_scale)), DataType::Int(32)); + const Expr max = Max(quantized_data, {axis}, true, false); + const Expr x = Subtract(quantized_data, max); + + const int n = 8; + const int m = 30; + const int bits = 8; + const Expr x_p = Subtract(Add(x, RightShift(x, MakeConstantScalar(DataType::Int(32), 1))), + RightShift(x, MakeConstantScalar(DataType::Int(32), 4))); + const Expr q = Divide(Negative(x_p), x_0); + const Expr r = Subtract(x_p, Multiply(q, Negative(x_0))); + const Expr x_b = Add(RightShift(r, MakeConstantScalar(DataType::Int(32), 1)), x_0); + const Expr exps = RightShift(LeftShift(x_b, MakeConstantScalar(DataType::Int(32), n)), q); + const Expr sums = Sum(exps, {axis}, true, false); + const Expr output = + RightShift(Multiply(Divide(MakeConstantScalar(DataType::Int(32), 1 << m), sums), exps), + MakeConstantScalar(DataType::Int(32), m - (bits - 1))); + const Expr requantized = + Requantize(output, arg_types[0].as<TensorTypeNode>()->shape, + MakeConstantScalar(DataType::Float(32), 1.f / (1 << (bits - 1))), + MakeConstantScalar(DataType::Int(32), 0), output_scale, output_zero_point, Review Comment: I suggest creating a helper function to clean up many `MakeConstantScalar(DataType::Int(32), ...)`. -- This is an automated message from the Apache Git Service. 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