66d9cddc83
* New updates. * Minor profiler updates Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
272 lines
12 KiB
C++
272 lines
12 KiB
C++
/***************************************************************************************************
|
|
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
* SPDX-License-Identifier: BSD-3-Clause
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions are met:
|
|
*
|
|
* 1. Redistributions of source code must retain the above copyright notice, this
|
|
* list of conditions and the following disclaimer.
|
|
*
|
|
* 2. Redistributions in binary form must reproduce the above copyright notice,
|
|
* this list of conditions and the following disclaimer in the documentation
|
|
* and/or other materials provided with the distribution.
|
|
*
|
|
* 3. Neither the name of the copyright holder nor the names of its
|
|
* contributors may be used to endorse or promote products derived from
|
|
* this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
|
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
|
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
|
|
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
|
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
|
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
|
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*
|
|
**************************************************************************************************/
|
|
/*! \file
|
|
\brief Implicit GEMM testbed sizes for Conv2d problem
|
|
*/
|
|
#pragma once
|
|
|
|
#include "../../common/cutlass_unit_test.h"
|
|
|
|
#include "cutlass/cutlass.h"
|
|
|
|
#include "cutlass/aligned_buffer.h"
|
|
#include "cutlass/numeric_types.h"
|
|
#include "cutlass/layout/matrix.h"
|
|
#include "cutlass/layout/tensor.h"
|
|
#include "cutlass/layout/pitch_linear.h"
|
|
#include "cutlass/core_io.h"
|
|
#include "cutlass/util/host_tensor.h"
|
|
#include "cutlass/util/tensor_view_io.h"
|
|
#include "cutlass/conv/convolution.h"
|
|
#include "cutlass/conv/conv2d_problem_size.h"
|
|
#include "cutlass/conv/conv3d_problem_size.h"
|
|
|
|
namespace test {
|
|
namespace conv {
|
|
namespace device {
|
|
|
|
using Conv3dProblemVector = std::vector<cutlass::conv::Conv3dProblemSize>;
|
|
|
|
////////////////////////////////////////////////////////////////////////////
|
|
/// Structure TestbedConv3dProblemSizes initializes and holds conv default and
|
|
/// important network sizes
|
|
////////////////////////////////////////////////////////////////////////////
|
|
struct TestbedConv3dProblemSizes {
|
|
|
|
//
|
|
// Data members
|
|
//
|
|
int minimum_channel_size;
|
|
Conv3dProblemVector conv3d_default_sizes;
|
|
Conv3dProblemVector conv3d_vnet_medical_sizes;
|
|
|
|
//
|
|
// Methods
|
|
//
|
|
/// Default ctor
|
|
TestbedConv3dProblemSizes(int minimum_channel_size_ = 64): minimum_channel_size (minimum_channel_size_) {
|
|
|
|
initialize_conv3d_default_sizes();
|
|
initialize_conv3d_vnet_medical_sizes(conv3d_vnet_medical_sizes, 1 /*batch-size*/);
|
|
|
|
filter_all();
|
|
}
|
|
|
|
/// Eliminates some illegal cases
|
|
void filter_all() {
|
|
|
|
Conv3dProblemVector *problems_vectors[] = {
|
|
&conv3d_default_sizes,
|
|
&conv3d_vnet_medical_sizes
|
|
};
|
|
|
|
for (Conv3dProblemVector *problems : problems_vectors) {
|
|
Conv3dProblemVector filtered;
|
|
|
|
for (cutlass::conv::Conv3dProblemSize const & problem : *problems) {
|
|
if (!(problem.C % minimum_channel_size)) {
|
|
filtered.push_back(problem);
|
|
}
|
|
}
|
|
|
|
*problems = filtered;
|
|
}
|
|
}
|
|
|
|
// Add a few standard convolution problem sizes
|
|
void initialize_conv3d_default_sizes() {
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 1, 3, 3, minimum_channel_size}, // input size (NDHWC)
|
|
{8, 1, 1, 1, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 1, 1, 8, minimum_channel_size}, // input size (NDHWC)
|
|
{8, 1, 1, 3, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 8, 8, 8, minimum_channel_size}, // input size (NDHWC)
|
|
{8, 3, 3, 3, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 16, 16, 16, minimum_channel_size}, // input size (NDHWC)
|
|
{8, 3, 3, 3, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 1, 15, 19, 160}, // input size (NDHWC)
|
|
{224, 1, 3, 6, 160}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 2, 1, 1, minimum_channel_size}, // input size (NDHWC)
|
|
{8, 2, 1, 1, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 1, 7, 7, minimum_channel_size}, // input size (NDHWC)
|
|
{16, 1, 3, 3, minimum_channel_size}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_default_sizes.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{1, 11, 15, 19, 64}, // input size (NDHWC)
|
|
{32, 4, 3, 6, 64}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({2, 1, 3}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
}
|
|
|
|
// Add vnet layers to unit testing sizes
|
|
void initialize_conv3d_vnet_medical_sizes(Conv3dProblemVector &conv3d_problem_vector, int batch_size = 1) {
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 32, 32, 32, 16}, // input size (NDHWC)
|
|
{32, 2, 2, 2, 16}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({2, 2, 2}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 16, 16, 16, 32}, // input size (NDHWC)
|
|
{32, 3, 3, 3, 32}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 16, 16, 16, 32}, // input size (NDHWC)
|
|
{64, 2, 2, 2, 32}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({2, 2, 2}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 8, 8, 8, 64}, // input size (NDHWC)
|
|
{64, 3, 3, 3, 64}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 8, 8, 8, 64}, // input size (NDHWC)
|
|
{128, 2, 2, 2, 64}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({2, 2, 2}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 4, 4, 4, 128}, // input size (NDHWC)
|
|
{128, 3, 3, 3, 128}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 8, 8, 8, 128}, // input size (NDHWC)
|
|
{128, 3, 3, 3, 128}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 16, 16, 16, 64}, // input size (NDHWC)
|
|
{64, 3, 3, 3, 64}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({1, 1, 1}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({1, 1, 1}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 32, 32, 32, 16}, // input size (NDHWC)
|
|
{64, 2, 2, 2, 16}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({2, 2, 2}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
|
|
conv3d_problem_vector.push_back(cutlass::conv::Conv3dProblemSize(
|
|
{batch_size, 16, 16, 16, 32}, // input size (NDHWC)
|
|
{128, 2, 2, 2, 32}, // filter size (KTRSC)
|
|
cutlass::Coord<3>({0, 0, 0}), // padding (pad_d, pad_h, pad_w)
|
|
cutlass::Coord<3>({2, 2, 2}), // stride (stride_d, stride_h, stride_w)
|
|
cutlass::Coord<3>({1, 1, 1}) // dilation (dilation_d, dilation_h, dilation_w)
|
|
));
|
|
|
|
}
|
|
|
|
};
|
|
|
|
} // namespace device
|
|
} // namespace conv
|
|
} // namespace test
|