245 lines
7.3 KiB
Plaintext
245 lines
7.3 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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#include <complex>
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#include "../common/cutlass_unit_test.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/layout/tensor.h"
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#include "cutlass/util/reference/device/tensor_reduce.h"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/host_tensor.h"
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////////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(TensorReduce, norm_rowmajor_f32) {
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int const kM = 129;
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int const kN = 91;
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cutlass::HostTensor<float, cutlass::layout::RowMajor> tensor({kM, kN});
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for (int m = 0; m < kM; ++m) {
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for (int n = 0; n < kN; ++n) {
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float x = float(((m * kN + m + 7) % 8) - 4);
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tensor.at({m, n}) = x;
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}
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}
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tensor.sync_device();
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double device_norm = cutlass::reference::device::TensorNorm(tensor.device_view(), double());
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double host_norm = cutlass::reference::host::TensorNorm(tensor.host_view(), double());
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EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001);
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}
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TEST(TensorReduce, norm_nhwc_f32) {
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int const kN = 19;
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int const kH = 18;
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int const kW = 17;
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int const kC = 16;
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cutlass::HostTensor<float, cutlass::layout::TensorNHWC> tensor({kN, kH, kW, kC});
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int idx = 0;
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double computed_norm = double();
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for (int n = 0; n < kN; ++n) {
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for (int h = 0; h < kH; ++h) {
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for (int w = 0; w < kW; ++w) {
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for (int c = 0; c < kC; ++c, ++idx) {
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float x = float(((idx + 7) % 8) - 4);
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computed_norm += double(x) * double(x);
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tensor.at({n, h, w, c}) = x;
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}
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}
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}
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}
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computed_norm = std::sqrt(computed_norm);
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tensor.sync_device();
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double device_norm = cutlass::reference::device::TensorNorm(tensor.device_view(), double());
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double host_norm = cutlass::reference::host::TensorNorm(tensor.host_view(), double());
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EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001 && std::abs(computed_norm - host_norm) < 0.001)
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<< "computed norm: " << computed_norm << "\n"
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<< " host norm: " << host_norm << "\n"
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<< "device norm: " << device_norm << "\n";
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}
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TEST(TensorReduce, norm_nhwc_f16) {
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int const kN = 69;
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int const kH = 68;
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int const kW = 67;
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int const kC = 66;
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cutlass::HostTensor<cutlass::half_t, cutlass::layout::TensorNHWC> tensor({kN, kH, kW, kC});
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int idx = 0;
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double computed_norm = double();
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for (int n = 0; n < kN; ++n) {
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for (int h = 0; h < kH; ++h) {
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for (int w = 0; w < kW; ++w) {
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for (int c = 0; c < kC; ++c, ++idx) {
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float x = float(((idx + 7) % 8) - 4);
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computed_norm += double(x) * double(x);
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tensor.at({n, h, w, c}) = cutlass::half_t(x);
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}
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}
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}
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}
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computed_norm = std::sqrt(computed_norm);
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tensor.sync_device();
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double device_norm = cutlass::reference::device::TensorNorm(tensor.device_view(), double());
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double host_norm = cutlass::reference::host::TensorNorm(tensor.host_view(), double());
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EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001 && std::abs(computed_norm - host_norm) < 0.001)
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<< "computed norm: " << computed_norm << "\n"
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<< " host norm: " << host_norm << "\n"
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<< "device norm: " << device_norm << "\n";
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}
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TEST(TensorReduce, norm_diff_nhwc_f32) {
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int const kN = 59;
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int const kH = 24;
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int const kW = 57;
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int const kC = 78;
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using Layout = cutlass::layout::TensorNHWC;
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cutlass::HostTensor<float, Layout> tensor_A({kN, kH, kW, kC});
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cutlass::HostTensor<float, Layout> tensor_B({kN, kH, kW, kC});
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int idx = 0;
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double sum_sq_diff = 0;
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for (int n = 0; n < kN; ++n) {
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for (int h = 0; h < kH; ++h) {
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for (int w = 0; w < kW; ++w) {
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for (int c = 0; c < kC; ++c, ++idx) {
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float a = float(((idx * 5 + 7) % 8) - 4);
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float b = float(((idx * 3 + 7) % 8) - 4);
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sum_sq_diff += double(a - b) * double(a - b);
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tensor_A.at({n, h, w, c}) = a;
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tensor_B.at({n, h, w, c}) = b;
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}
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}
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}
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}
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tensor_A.sync_device();
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tensor_B.sync_device();
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double device_norm = cutlass::reference::device::TensorNormDiff(
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tensor_A.device_view(), tensor_B.device_view(), double());
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double host_norm = std::sqrt(sum_sq_diff);
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EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001f)
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<< " host norm: " << host_norm << "\n"
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<< "device norm: " << device_norm;
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}
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TEST(TensorReduce, norm_diff_nhwc_f16) {
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int const kN = 59;
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int const kH = 24;
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int const kW = 57;
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int const kC = 78;
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using Layout = cutlass::layout::TensorNHWC;
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cutlass::HostTensor<cutlass::half_t, Layout> tensor_A({kN, kH, kW, kC});
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cutlass::HostTensor<cutlass::half_t, Layout> tensor_B({kN, kH, kW, kC});
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int idx = 0;
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double sum_sq_diff = 0;
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for (int n = 0; n < kN; ++n) {
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for (int h = 0; h < kH; ++h) {
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for (int w = 0; w < kW; ++w) {
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for (int c = 0; c < kC; ++c, ++idx) {
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float a = float(((idx * 5 + 7) % 8) - 4);
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float b = float(((idx * 3 + 7) % 8) - 4);
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sum_sq_diff += double(a - b) * double(a - b);
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tensor_A.at({n, h, w, c}) = cutlass::half_t(a);
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tensor_B.at({n, h, w, c}) = cutlass::half_t(b);
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}
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}
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}
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}
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tensor_A.sync_device();
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tensor_B.sync_device();
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double device_norm = cutlass::reference::device::TensorNormDiff(
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tensor_A.device_view(), tensor_B.device_view(), double());
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double host_norm = std::sqrt(sum_sq_diff);
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EXPECT_TRUE(std::abs(host_norm - device_norm) < 0.001f)
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<< " host norm: " << host_norm << "\n"
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<< "device norm: " << device_norm;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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