mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-13 09:45:56 +00:00
12a8883c48
* adding files for F32 example * adding functioning implementation with scalar multiplication and unary operator support * added fp 16 type check in unary square * updating scalar multiplication as an operator * functioning version with scalar operator * changing strides for col major * updated column major implementation * working column major implementation * cleaned up comments, rearranged/renamed files * small edits to 3d transpose profiler * adding test/profiler/instance files for hipTensor permute unit test * added more test instances * cleaned up errors, randomized input tensor, added more instances * turned off time printouts * removed conflicting transpose profiler * rearranged some files
213 lines
8.6 KiB
C++
213 lines
8.6 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iomanip>
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#include <random>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_elementwise_scale.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_scale_impl.hpp"
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#include "ck/library/tensor_operation_instance/gpu/permute_scale.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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namespace ck {
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template <typename HostTensorA, typename HostTensorB, typename FunctorA, typename FunctorB>
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void host_elementwise4D(HostTensorB& B_nhwc,
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const HostTensorA& A_nchw,
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FunctorA functor_a,
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FunctorB functor_b,
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float scale)
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{
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std::size_t N = A_nchw.mDesc.GetLengths()[0];
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std::size_t C = A_nchw.mDesc.GetLengths()[1];
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std::size_t H = A_nchw.mDesc.GetLengths()[2];
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std::size_t W = A_nchw.mDesc.GetLengths()[3];
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for(std::size_t w = 0; w < W; ++w)
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for(std::size_t h = 0; h < H; ++h)
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for(std::size_t c = 0; c < C; ++c)
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for(std::size_t n = 0; n < N; ++n)
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{
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using tmp_type = ck::remove_reference_t<decltype(B_nhwc(0, 0))>;
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tmp_type tmp_val = 0;
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auto a_val = A_nchw.mData[(n) + (c * N) + (h * C * N) + (w * H * C * N)];
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functor_b(tmp_val, a_val);
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functor_a(B_nhwc.mData[(n) + (c * W * H * N) + (h * N) + (w * H * N)],
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scale * tmp_val);
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}
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}
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template <typename ADataType, typename BDataType, index_t NumDim>
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bool test_permute_scale_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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std::vector<index_t> lengths)
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{
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bool pass = true;
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using ElementOp = ck::tensor_operation::element_wise::PassThrough;
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using UnaryOp = ck::tensor_operation::element_wise::UnarySquare;
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using Scale = ck::tensor_operation::element_wise::Scale;
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float scale = 2.f;
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index_t N = lengths[0];
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index_t C = lengths[1];
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index_t H = lengths[2];
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index_t W = lengths[3];
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std::vector<ck::index_t> nchw = {N, C, H, W};
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std::vector<ck::index_t> nhwc = {N, H, W, C};
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Tensor<ADataType> a(nchw);
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Tensor<BDataType> b(nhwc);
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Tensor<BDataType> host_b(nhwc);
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std::array<ck::index_t, 4> ab_lengths;
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std::array<ck::index_t, 4> a_strides = {1,
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static_cast<int>(nchw[0]),
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static_cast<int>(nchw[0] * nchw[1]),
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static_cast<int>(nchw[0] * nchw[1] * nchw[2])};
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std::array<ck::index_t, 4> b_strides = {1,
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static_cast<int>(nhwc[0] * nhwc[1] * nhwc[2]),
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static_cast<int>(nhwc[0]),
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static_cast<int>(nhwc[0] * nhwc[1])};
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ck::ranges::copy(nchw, ab_lengths.begin());
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std::cout << "A: " << a.mDesc << std::endl;
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std::cout << "B: " << b.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1: a.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2}); break;
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default: // a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}
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std::mt19937 gen(11939);
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std::uniform_int_distribution<int> dis(0, 1);
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auto i = 0;
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for(std::size_t w = 0; w < a.mDesc.GetLengths()[3]; ++w)
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for(std::size_t h = 0; h < a.mDesc.GetLengths()[2]; ++h)
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for(std::size_t c = 0; c < a.mDesc.GetLengths()[1]; ++c)
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for(std::size_t n = 0; n < a.mDesc.GetLengths()[0]; ++n)
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{
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a.mData[(n * nchw[1] * nchw[2] * nchw[3]) + (c * nchw[2] * nchw[3]) +
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(h * nchw[3]) + w] = i;
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i = dis(gen);
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}
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a.mData.data());
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std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
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using DeviceOp = ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
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ck::Tuple<BDataType>,
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ElementOp,
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UnaryOp,
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Scale,
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NumDim>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_instance_name;
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float best_ave_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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if(do_verification)
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{
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host_elementwise4D(host_b, a, ElementOp{}, UnaryOp{}, scale);
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}
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr = op_ptr->MakeArgumentPointer(ab_lengths,
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{a_strides},
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{b_strides},
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input,
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output,
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ElementOp{},
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UnaryOp{},
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Scale{scale});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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b_device_buf.SetZero();
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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if(do_verification)
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{
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b_device_buf.FromDevice(b.mData.data());
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pass &= ck::utils::check_err(
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b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b.mData, ",") << std::endl;
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}
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}
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std::string op_name = op_ptr->GetTypeString();
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float ave_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
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std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
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sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_instance_name = op_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
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}
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}
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if(time_kernel)
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{
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LogRange(std::cout << "length = ", lengths, ",") << ", ";
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std::cout << "best perf = " << best_ave_time << " ms, " << best_gb_per_sec << " GB/s, "
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<< best_instance_name << std::endl;
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}
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return true;
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}
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} // namespace ck
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