733 lines
24 KiB
C++
733 lines
24 KiB
C++
/***************************************************************************************************
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* Copyright (c) 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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/*! \file
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\brief Implicit GEMM for fused epilogue broadcast testbed
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Parallel split-k is not tested because we can just use regular conv kernel
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when we need to use parallel-splitk. Broadcast can happen in the reduction
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kernel.
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*/
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#pragma once
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#include <fstream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/cutlass.h"
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#include "cutlass/conv/device/implicit_gemm_convolution.h"
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#include "cutlass/reduction/device/reduce_split_k.h"
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#include "cutlass/reduction/thread/reduction_operators.h"
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#include "conv3d_problems.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/device/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/convolution.h"
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#include "cutlass/util/reference/device/convolution.h"
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#include "cutlass/core_io.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "../cache_testbed_output.h"
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namespace test {
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namespace conv {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Conv3d>
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struct Conv3dWithBroadcastReferenceOp {
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using OutputOp = typename Conv3d::EpilogueOutputOp;
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using ElementCompute = typename OutputOp::ElementCompute;
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using ElementZ = typename OutputOp::ElementZ;
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using ElementT = typename OutputOp::ElementT;
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typename OutputOp::BinaryOp binary_op;
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typename OutputOp::ElementwiseOp elementwise_op;
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Conv3dWithBroadcastReferenceOp() { }
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void operator()(ElementZ &Z, ElementT &T, ElementCompute conv3d, ElementCompute bias) {
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ElementCompute t_full = binary_op(conv3d, bias);
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T = ElementT(t_full);
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ElementCompute z_full = elementwise_op(t_full);
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Z = ElementZ(z_full);
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}
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};
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/////////////////////////////////////////////////////////////////////////////////////////////////
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// Fused testbed
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//
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// Y = CONV(AB, C)
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//
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// T[n, o, p, q, k] = ReductionOp(Y[n, o, p, q, k], Broadcast[k])
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//
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// Z[n, o, p, q, k] = Elementwise(T[n, o, p, q, k])
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//
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template <
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typename Conv3d,
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typename ReferenceOp,
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bool AddBroadcastFirst = false
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>
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class TestbedConv3dWithBroadcast {
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public:
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using ElementA = typename Conv3d::ElementA;
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using LayoutA = typename Conv3d::LayoutA;
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using ElementB = typename Conv3d::ElementB;
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using LayoutB = typename Conv3d::LayoutB;
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using ElementC = typename Conv3d::ElementC;
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using LayoutC = typename Conv3d::LayoutC;
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using ElementAccumulator = typename Conv3d::ElementAccumulator;
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using ElementCompute = typename Conv3d::ElementCompute;
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using EpilogueOutputOp = typename Conv3d::EpilogueOutputOp;
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using ElementZ = typename EpilogueOutputOp::ElementZ;
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using ElementT = typename EpilogueOutputOp::ElementT;
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using ElementVector = typename EpilogueOutputOp::ElementVector;
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static cutlass::conv::Operator const kConvolutionalOperator = Conv3d::kConvolutionalOperator;
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static const bool kAddBroadcastFirst = AddBroadcastFirst;
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static const bool kStoreT = EpilogueOutputOp::kStoreT;
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public:
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/// Initialization
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cutlass::Distribution::Kind init_A;
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cutlass::Distribution::Kind init_B;
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cutlass::Distribution::Kind init_C;
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uint64_t seed;
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cutlass::HostTensor<ElementA, LayoutA> tensor_A;
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cutlass::HostTensor<ElementB, LayoutB> tensor_B;
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cutlass::HostTensor<ElementC, LayoutC> tensor_C;
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cutlass::HostTensor<ElementAccumulator, LayoutC> tensor_C_reference;
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cutlass::HostTensor<ElementZ, LayoutC> tensor_Z_computed;
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cutlass::HostTensor<ElementZ, LayoutC> tensor_Z_reference;
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cutlass::HostTensor<ElementT, LayoutC> tensor_T_computed;
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cutlass::HostTensor<ElementT, LayoutC> tensor_T_reference;
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cutlass::HostTensor<ElementAccumulator, LayoutC> tensor_Y_reference;
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cutlass::HostTensor<ElementVector, LayoutC> tensor_Broadcast; // Input Broadcast
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public:
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TestbedConv3dWithBroadcast(
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cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
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cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
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uint64_t seed_ = 2080
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):
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init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) {
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}
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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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void initialize_tensor(
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cutlass::TensorView<Element, Layout> view,
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cutlass::Distribution::Kind dist_kind,
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uint64_t seed) {
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if (dist_kind == cutlass::Distribution::Uniform) {
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int scope;
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int bits = cutlass::sizeof_bits<Element>::value;
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if (bits <= 8) {
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scope = 2;
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}
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else if (bits == 16) {
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if (cutlass::sizeof_bits<ElementAccumulator>::value <= 16) {
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scope = 3;
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}
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else {
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scope = 5;
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}
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}
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else {
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scope = 8;
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}
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope, -scope, 0);
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}
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else if (dist_kind == cutlass::Distribution::Identity) {
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cutlass::reference::host::TensorFillIdentity(view);
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}
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else if (dist_kind == cutlass::Distribution::Gaussian) {
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cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
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}
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else if (dist_kind == cutlass::Distribution::Sequential) {
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cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
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}
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else {
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}
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}
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void initialize(
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cutlass::conv::Conv3dProblemSize const &problem_size, bool non_packed_test = false, uint64_t seed = 2019) {
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// to make the layout of tensors a little bit bigger than the problem size
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cutlass::Tensor5DCoord stride_increment = cutlass::Tensor5DCoord(8, 16, 32, 32, 64);
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cutlass::Tensor5DCoord tensor_A_extent = implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size);
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cutlass::Tensor5DCoord tensor_B_extent = implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size);
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cutlass::Tensor5DCoord tensor_C_extent = implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size);
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if (non_packed_test) {
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tensor_A_extent += stride_increment;
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tensor_C_extent += stride_increment;
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}
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tensor_A.resize(tensor_A_extent);
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tensor_B.resize(tensor_B_extent);
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tensor_C.resize(tensor_C_extent);
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tensor_C_reference.resize(tensor_C_extent);
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tensor_Z_computed.resize(tensor_C_extent);
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tensor_Z_reference.resize(tensor_C_extent);
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tensor_T_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
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tensor_T_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size));
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tensor_Y_reference.resize(tensor_C_extent);
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tensor_Broadcast.resize({
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1,
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1,
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1,
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1,
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implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size).c(),
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});
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initialize_tensor(tensor_A.host_view(), init_A, seed);
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initialize_tensor(tensor_B.host_view(), init_B, seed * 17);
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initialize_tensor(tensor_C.host_view(), init_C, seed * 39);
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initialize_tensor(tensor_Broadcast.host_view(), init_C, seed * 39);
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for (int n = 0; n < tensor_C_reference.extent().n(); ++n) {
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for (int o = 0; o < tensor_C_reference.extent().d(); ++o) {
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for (int p = 0; p < tensor_C_reference.extent().h(); ++p) {
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for (int q = 0; q < tensor_C_reference.extent().w(); ++q) {
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for (int k = 0; k < tensor_C_reference.extent().c(); ++k) {
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tensor_C_reference.at({n, o, p, q, k}) = ElementAccumulator(tensor_C.at({n, o, p, q, k}));
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}
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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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tensor_C.sync_device();
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tensor_Broadcast.sync_device();
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tensor_C_reference.sync_device();
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tensor_Z_computed.sync_device();
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tensor_Z_reference.sync_device();
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tensor_T_computed.sync_device();
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tensor_T_reference.sync_device();
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tensor_Y_reference.sync_device();
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}
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bool sufficient() const {
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//
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// Determine SMEM requirements and waive if not satisfied
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//
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size_t smem_size = sizeof(typename Conv3d::UnderlyingKernel::SharedStorage);
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cudaDeviceProp properties;
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int device_idx;
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cudaError_t result = cudaGetDevice(&device_idx);
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDevice() API call failed.");
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}
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result = cudaGetDeviceProperties(&properties, device_idx);
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if (result != cudaSuccess) {
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throw std::runtime_error("cudaGetDeviceProperties() failed");
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}
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if (properties.sharedMemPerBlockOptin < smem_size) {
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return false;
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}
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return true;
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}
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/// Executes one test
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bool run(
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cutlass::conv::Conv3dProblemSize const &problem_size,
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cutlass::conv::SplitKMode const &split_k_mode = cutlass::conv::SplitKMode::kSerial,
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bool non_packed_test = false,
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ElementCompute alpha = ElementCompute(1),
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ElementCompute beta = ElementCompute(1)) {
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// Waive test if insufficient CUDA device
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if (!sufficient()) {
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if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
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std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
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}
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return true;
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}
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#if 0 //display conv3d problem size for debugging
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std::cout << problem_size << std::endl
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<< "alpha, beta: (" << alpha << ", " << beta << ")" << std::endl
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<< "split_k_mode: " << ((split_k_mode == cutlass::conv::SplitKMode::kSerial) ? "(serial)" : "(parallel)") << std::endl
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<< std::endl;
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#endif
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initialize(problem_size, non_packed_test);
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// configure the operator
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Conv3d conv3d_op;
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typename Conv3d::Arguments conv3d_args(
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problem_size,
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tensor_A.device_ref(),
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tensor_B.device_ref(),
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tensor_C.device_ref(),
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tensor_Z_computed.device_ref(),
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{alpha, beta},
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split_k_mode,
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tensor_Broadcast.device_data(),
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kStoreT ? tensor_T_computed.device_data() : nullptr,
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0, // This must be zero
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implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size).c()
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);
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// initialize the kernel
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size_t workspace_size = Conv3d::get_workspace_size(conv3d_args);
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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cutlass::Status status = conv3d_op.initialize(conv3d_args, workspace.get());
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if (status != cutlass::Status::kSuccess) {
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cudaError_t error = cudaGetLastError();
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std::cerr << "This test is not supported: " << cudaGetErrorString(error) << "\n";
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return true;
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}
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// run conv3d operator
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status = conv3d_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess);
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if (status != cutlass::Status::kSuccess) {
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return false;
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}
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bool passed = false;
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cudaError_t result = cudaDeviceSynchronize();
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EXPECT_EQ(result, cudaSuccess) << " device reference error: "
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<< cudaGetErrorString(result);
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tensor_T_computed.sync_host();
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tensor_Z_computed.sync_host();
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//
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// Reference check
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//
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// When kAddBroadcastFirst is true, add bias on the host
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ElementCompute beta_ref = kAddBroadcastFirst ? ElementCompute(0) : beta;
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#if CUTLASS_CONV_TEST_UNIT_REFERENCE_DEVICE_ENABLED
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cutlass::reference::device::Conv3d<
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementAccumulator,
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LayoutC,
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ElementAccumulator,
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ElementAccumulator
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>(
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kConvolutionalOperator,
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problem_size,
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tensor_A.device_ref(),
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tensor_B.device_ref(),
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tensor_C_reference.device_ref(),
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tensor_Y_reference.device_ref(),
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alpha,
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beta_ref);
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// sync host (copy device data to host) for dumping error output in case of mismatches
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tensor_Y_reference.sync_host();
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#else
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cutlass::reference::host::Conv3d<
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ElementA,
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LayoutA,
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ElementB,
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LayoutB,
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ElementAccumulator,
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LayoutC,
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ElementAccumulator,
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ElementAccumulator
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>(
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kConvolutionalOperator,
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problem_size,
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tensor_A.host_ref(),
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tensor_B.host_ref(),
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tensor_C_reference.host_ref(),
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tensor_Y_reference.host_ref(),
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alpha,
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beta_ref);
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#endif
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ReferenceOp reference_op;
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// compute tensor Z and tensor T
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for (int n = 0; n < problem_size.N; ++n) {
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for (int o = 0; o < (kConvolutionalOperator == cutlass::conv::Operator::kFprop ? problem_size.Z : problem_size.D); ++o) {
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for (int p = 0; p < (kConvolutionalOperator == cutlass::conv::Operator::kFprop ? problem_size.P : problem_size.H); ++p) {
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for (int q = 0; q < (kConvolutionalOperator == cutlass::conv::Operator::kFprop ? problem_size.Q : problem_size.W); ++q) {
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for (int k = 0; k < (kConvolutionalOperator == cutlass::conv::Operator::kFprop ? problem_size.K : problem_size.C); ++k) {
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ElementZ z{};
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ElementT t{};
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ElementCompute accum = tensor_Y_reference.at({n, o, p, q, k});
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ElementCompute bias = ElementCompute(tensor_Broadcast.at({0, 0, 0, 0, k}));
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if (kAddBroadcastFirst) {
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reference_op(z, t, accum + bias,
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beta * ElementCompute(tensor_C_reference.at({n, o, p, q, k})));
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} else {
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reference_op(z, t, accum, bias);
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}
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tensor_Z_reference.at({n, o, p, q, k}) = z;
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tensor_T_reference.at({n, o, p, q, k}) = t;
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}
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}
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}
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}
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}
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if (kStoreT) {
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passed = cutlass::reference::host::TensorEquals(
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tensor_T_computed.host_view(),
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tensor_T_reference.host_view());
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EXPECT_TRUE(passed);
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}
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passed = cutlass::reference::host::TensorEquals(
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tensor_Z_computed.host_view(),
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tensor_Z_reference.host_view());
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EXPECT_TRUE(passed);
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if (!passed) {
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std::stringstream fname;
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fname << "error_Conv3d_ImplicitGemm_device_"
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<< (split_k_mode == cutlass::conv::SplitKMode::kSerial ? "serial_reduction_" : "parallel_reduction_")
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<< (Conv3d::kConvolutionalOperator == cutlass::conv::Operator::kFprop ? "fprop_" :
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(Conv3d::kConvolutionalOperator == cutlass::conv::Operator::kDgrad ? "dgrad_" :
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(Conv3d::kConvolutionalOperator == cutlass::conv::Operator::kDeconv ? "deconv_" : "wgrad_")))
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<< "nnhwc_"
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<< problem_size.N << "x"
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<< problem_size.D << "x"
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<< problem_size.H << "x"
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<< problem_size.W << "x"
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<< problem_size.C
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<< "_krsc_"
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<< problem_size.K << "x"
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<< problem_size.T << "x"
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<< problem_size.R << "x"
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<< problem_size.S << "x"
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<< problem_size.C
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<< "_padding_"
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<< problem_size.pad_d << "x"
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<< problem_size.pad_h << "x"
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<< problem_size.pad_w
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<< "_stride_"
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<< problem_size.stride_d << "x"
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<< problem_size.stride_h << "x"
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<< problem_size.stride_w
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<< "_dilation_"
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<< problem_size.dilation_d << "x"
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<< problem_size.dilation_h << "x"
|
|
<< problem_size.dilation_w << "_"
|
|
<< (problem_size.mode == cutlass::conv::Mode::kCrossCorrelation ? "xcorr_" : "conv_")
|
|
<< (non_packed_test ? "non_packed_tensor_test_" : "packed_tensor_test_")
|
|
<< Conv3d::ThreadblockShape::kM << "x"
|
|
<< Conv3d::ThreadblockShape::kN << "x"
|
|
<< Conv3d::ThreadblockShape::kK << "_"
|
|
<< Conv3d::WarpShape::kM << "x"
|
|
<< Conv3d::WarpShape::kN << "x"
|
|
<< Conv3d::WarpShape::kK << ".txt";
|
|
|
|
std::cout << fname.str() << std::endl;
|
|
|
|
std::ofstream results(fname.str());
|
|
|
|
results << problem_size << std::endl;
|
|
|
|
results
|
|
<< "\nA:\n" << tensor_A.host_view() << "\n"
|
|
<< "\nB:\n" << tensor_B.host_view() << "\n"
|
|
<< "\nC:\n" << tensor_C.host_view() << "\n"
|
|
<< "\nBroadcast:\n" << tensor_Broadcast.host_view() << "\n"
|
|
<< "\nY reference:\n" << tensor_Y_reference.host_view() << "\n"
|
|
<< "\nT reference:\n" << tensor_T_reference.host_view() << "\n"
|
|
<< "\nT computed:\n" << tensor_T_computed.host_view() << "\n"
|
|
<< "\nZ reference:\n" << tensor_Z_reference.host_view() << "\n"
|
|
<< "\nZ computed:\n" << tensor_Z_computed.host_view() << "\n";
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
// TestAllConv: Runs cutlass::conv::device::ImplicitGemmConvolution operator and compares it with reference
|
|
// TestAllConv runs conv operator on default conv problem sizes from test::conv::device::TestbedConv3dProblemSizes
|
|
// Additionally, each conv3d test can provide conv problem sizes (conv_test_sizes) and blacklist of sizes
|
|
// (conv_blacklist_sizes)
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
template <typename ImplicitGemm,
|
|
typename ReferenceOp = Conv3dWithBroadcastReferenceOp<ImplicitGemm>,
|
|
bool AddBroadcastFirst = false,
|
|
bool TestSplitK = true
|
|
>
|
|
bool TestAllConv3dWithBroadcast(
|
|
const Conv3dProblemVector &conv_test_sizes = Conv3dProblemVector(),
|
|
const Conv3dProblemVector &conv_blacklist_sizes = Conv3dProblemVector(),
|
|
bool non_packed_test = false) {
|
|
|
|
bool passed = true;
|
|
|
|
//
|
|
// Testbed object
|
|
//
|
|
|
|
TestbedConv3dWithBroadcast<ImplicitGemm, ReferenceOp, AddBroadcastFirst> testbed;
|
|
|
|
//
|
|
// Get conv problem sizes to run conv operator
|
|
//
|
|
TestbedConv3dProblemSizes conv3d_problems(128/cutlass::sizeof_bits<typename ImplicitGemm::ElementA>::value);
|
|
|
|
// Vector of conv3d problem sizes to avoid duplicate runs
|
|
Conv3dProblemVector conv_tested_sizes;
|
|
|
|
Conv3dProblemVector const *problem_vectors[] = {
|
|
&conv3d_problems.conv3d_default_sizes,
|
|
&conv3d_problems.conv3d_vnet_medical_sizes,
|
|
&conv_test_sizes
|
|
};
|
|
|
|
// Sweep conv3d problem sizes (split-k-mode=kSerial, split-k-slice=1, alpha=1.0, beta=0.0)
|
|
for (Conv3dProblemVector const * problem_vector : problem_vectors) {
|
|
|
|
// Run conv testbed on default convolution sizes
|
|
for(auto conv_problem : *problem_vector) {
|
|
|
|
// Skip blacklist and avoid duplicate problem sizes
|
|
if (std::find(conv_blacklist_sizes.begin(), conv_blacklist_sizes.end(), conv_problem) != conv_blacklist_sizes.end() ||
|
|
std::find(conv_tested_sizes.begin(), conv_tested_sizes.end(), conv_problem) != conv_tested_sizes.end()) {
|
|
continue;
|
|
}
|
|
|
|
//
|
|
// Procedurally disable certain cases
|
|
//
|
|
|
|
// CUTLASS DGRAD's *unity* stride specialization only support stride {1, 1}
|
|
if ((ImplicitGemm::kConvolutionalOperator == cutlass::conv::Operator::kDgrad ||
|
|
ImplicitGemm::kConvolutionalOperator == cutlass::conv::Operator::kDeconv) &&
|
|
(ImplicitGemm::UnderlyingKernel::Mma::IteratorA::kStrideSupport ==
|
|
cutlass::conv::StrideSupport::kUnity)) {
|
|
if (!((conv_problem.stride_d == 1) &&
|
|
(conv_problem.stride_h == 1) &&
|
|
(conv_problem.stride_w == 1))
|
|
) {
|
|
continue;
|
|
}
|
|
}
|
|
|
|
#if 0 // relax restrictions on analytic strided dgrad
|
|
// CUTLASS DGRAD's *strided* specialization only support stride >= {2, 2}
|
|
if ((ImplicitGemm::kConvolutionalOperator == cutlass::conv::Operator::kDgrad ||
|
|
ImplicitGemm::kConvolutionalOperator == cutlass::conv::Operator::kDeconv) &&
|
|
(ImplicitGemm::UnderlyingKernel::Mma::IteratorA::kStrideSupport ==
|
|
cutlass::conv::StrideSupport::kStrided)) {
|
|
if (((conv_problem.stride_d == 1) && (conv_problem.stride_h == 1) && (conv_problem.stride_w == 1))) {
|
|
continue;
|
|
}
|
|
}
|
|
#endif
|
|
|
|
//
|
|
// Test
|
|
//
|
|
// push back tested problem size to avoid re-running duplicates
|
|
conv_tested_sizes.push_back(conv_problem);
|
|
|
|
// test mode = xcross
|
|
passed = testbed.run(
|
|
conv_problem,
|
|
cutlass::conv::SplitKMode::kSerial, non_packed_test);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
|
|
// test mode = convolution
|
|
passed = testbed.run(
|
|
conv_problem.reset_mode(cutlass::conv::Mode::kConvolution),
|
|
cutlass::conv::SplitKMode::kSerial, non_packed_test);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (!TestSplitK)
|
|
return passed;
|
|
|
|
// Sweep split-k-slice using serial and prallel reduction with non-unity alpha and non-zero beta for
|
|
// a single conv3d problem size. Convolution unit tests take a long time to run so only sweep parameters
|
|
// which are abolutely necessary to catch functional bugs. The below code does provide option to sweep
|
|
// alpha and beta for local testing, but only runs one value for alpha and beta.
|
|
cutlass::conv::Conv3dProblemSize conv3d_split_k_test_size (
|
|
{1, 8, 8, 8, 32}, // input size (NDHWC)
|
|
{32, 3, 3, 3, 32}, // 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)
|
|
);
|
|
|
|
cutlass::conv::SplitKMode split_k_modes [] = {
|
|
cutlass::conv::SplitKMode::kSerial
|
|
};
|
|
|
|
int split_k_slices[] = {
|
|
1, 2, 3, 4, 201
|
|
};
|
|
|
|
double problem_alpha[] = {
|
|
2.0
|
|
};
|
|
|
|
double problem_beta[] = {
|
|
2.0
|
|
};
|
|
|
|
for (auto split_k_mode : split_k_modes) {
|
|
for (auto split_k_slice : split_k_slices) {
|
|
for (auto alpha : problem_alpha) {
|
|
for (auto beta : problem_beta) {
|
|
|
|
passed = testbed.run(
|
|
conv3d_split_k_test_size.reset_split_k_slices(split_k_slice),
|
|
split_k_mode,
|
|
false,/*non_packed_test*/
|
|
cutlass::from_real<typename ImplicitGemm::ElementCompute>(alpha),
|
|
cutlass::from_real<typename ImplicitGemm::ElementCompute>(beta));
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
|
|
template <typename ImplicitGemm,
|
|
typename ReferenceOp = Conv3dWithBroadcastReferenceOp<ImplicitGemm>,
|
|
bool AddBroadcastFirst = false>
|
|
bool TestSpecificConv3dWithBroadcast(
|
|
const Conv3dProblemVector & problem_sizes,
|
|
bool non_packed_test = false) {
|
|
|
|
bool passed = true;
|
|
|
|
//
|
|
// Testbed object
|
|
//
|
|
|
|
TestbedConv3dWithBroadcast<ImplicitGemm, ReferenceOp, AddBroadcastFirst> testbed;
|
|
|
|
// Sweep conv3d problem sizes (split-k-mode=kSerial, split-k-slice=1, alpha=1.0, beta=0.0)
|
|
for(auto conv_problem : problem_sizes) {
|
|
|
|
//
|
|
// Test
|
|
//
|
|
|
|
// test mode = xcross, non_packed_test = false
|
|
passed = testbed.run(
|
|
conv_problem,
|
|
cutlass::conv::SplitKMode::kSerial, non_packed_test);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
|
|
// test mode = convolution, non_packed_test = false
|
|
passed = testbed.run(
|
|
conv_problem.reset_mode(cutlass::conv::Mode::kConvolution),
|
|
cutlass::conv::SplitKMode::kSerial, non_packed_test);
|
|
|
|
if (!passed) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace conv
|
|
} // namespace test
|