652 lines
20 KiB
C++
652 lines
20 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, 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 Tests for device-wide GEMM interface
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*/
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#pragma once
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include "../../common/cutlass_unit_test.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/tensor_copy.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/reference/host/gemm.h"
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#include "cutlass/util/reference/host/gemm_complex.h"
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#include "testbed_utils.h"
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namespace test {
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namespace gemm {
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namespace device {
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/////////////////////////////////////////////////////////////////////////////////////////////////
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template <typename Gemm>
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struct GemmWithBroadcastReferenceOp {
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using OutputOp = typename Gemm::GemmKernel::Epilogue::OutputOp;
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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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GemmWithBroadcastReferenceOp() { }
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void operator()(ElementZ &Z, ElementT &T, ElementCompute gemm, ElementCompute bias) {
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ElementCompute z_full = binary_op(gemm, bias);
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Z = ElementZ(z_full);
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ElementCompute t_full = elementwise_op(z_full);
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T = ElementT(t_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 = GEMM(AB, C)
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//
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// Z[i, j] = ReductionOp(Y[i, j], Broadcast[i])
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//
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// T[i, j] = Elementwise(Z[i, j])
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//
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template <
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typename Gemm,
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typename ReferenceOp = GemmWithBroadcastReferenceOp<Gemm>
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>
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struct TestbedGemmWithBroadcast {
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using OutputOp = typename Gemm::GemmKernel::Epilogue::OutputOp;
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using ElementC = typename Gemm::ElementC;
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using ElementAccumulator = typename Gemm::ElementAccumulator;
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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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/// 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<typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A; // Input A
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cutlass::HostTensor<typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B; // Input B
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cutlass::HostTensor<ElementC, typename Gemm::LayoutC> tensor_C; // Input C
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cutlass::HostTensor<ElementC, typename Gemm::LayoutC> tensor_Broadcast; // Input Broadcast
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cutlass::HostTensor<ElementZ, typename Gemm::LayoutC> tensor_Z;
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cutlass::HostTensor<ElementT, typename Gemm::LayoutC> tensor_T;
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cutlass::HostTensor<ElementAccumulator, typename Gemm::LayoutC> tensor_C_ref;
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cutlass::HostTensor<ElementAccumulator, typename Gemm::LayoutC> tensor_Y_ref;
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cutlass::HostTensor<ElementZ, typename Gemm::LayoutC> tensor_Z_ref;
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cutlass::HostTensor<ElementT, typename Gemm::LayoutC> tensor_T_ref;
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//
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// Methods
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//
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TestbedGemmWithBroadcast(
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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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/// Helper to initialize a tensor view
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template <typename Element, typename Layout>
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bool 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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double scope_max, scope_min;
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int bits_input = cutlass::sizeof_bits<Element>::value;
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int bits_output = cutlass::sizeof_bits<typename Gemm::ElementC>::value;
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if (bits_input == 1) {
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scope_max = 2;
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scope_min = 0;
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} else if (bits_input <= 8) {
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scope_max = 2;
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scope_min = -2;
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} else if (bits_output == 16) {
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scope_max = 5;
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scope_min = -5;
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} else {
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scope_max = 8;
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scope_min = -8;
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}
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cutlass::reference::host::TensorFillRandomUniform(
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view, seed, scope_max, scope_min, 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(
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view.data(), view.capacity());
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}
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else {
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// TODO: Implement the rest
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EXPECT_TRUE(false) << "Not implemented";
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return false;
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}
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return true;
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}
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/// Initializes data structures
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void initialize(cutlass::gemm::GemmCoord problem_size) {
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//
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// Allocate the GEMM workspace
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//
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tensor_A.resize(problem_size.mk());
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tensor_B.resize(problem_size.kn());
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tensor_C.resize(problem_size.mn());
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tensor_Z.resize(problem_size.mn());
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tensor_T.resize(problem_size.mn());
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tensor_Broadcast.resize({
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problem_size.m(),
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1
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});
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tensor_C_ref.resize(problem_size.mn());
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tensor_Y_ref.resize(problem_size.mn());
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tensor_Z_ref.resize(problem_size.mn());
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tensor_T_ref.resize(problem_size.mn());
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EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019));
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EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2018));
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EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2017));
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EXPECT_TRUE(initialize_tensor(tensor_Broadcast.host_view(), init_C, seed + 2020));
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// It is possible to randomly initialize to all zeros, so override this with non-zeros
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// in the upper left corner of each operand.
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tensor_A.host_view().at({0, 0}) = typename Gemm::ElementA(1);
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tensor_B.host_view().at({0, 0}) = typename Gemm::ElementB(1);
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tensor_C.host_view().at({0, 0}) = typename Gemm::ElementC(1);
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for (int m = 0; m < tensor_C_ref.extent().row(); ++m) {
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for (int n = 0; n < tensor_C_ref.extent().column(); ++n) {
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tensor_C_ref.at({m, n}) = ElementAccumulator(tensor_C.at({m, n}));
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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_Z.sync_device();
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tensor_T.sync_device();
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}
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/// Compares computed reference with device reference and outputs to a file if incorrect
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bool compare_reference(
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cutlass::gemm::GemmCoord problem_size,
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ElementAccumulator alpha,
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ElementAccumulator beta) {
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tensor_Z.sync_host();
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tensor_T.sync_host();
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_A.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_B.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_C.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_Z.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_T.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_Z_ref.host_view()), 0);
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EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_T_ref.host_view()), 0);
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bool passed = true;
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float norm_diff = 0;
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if (OutputOp::kStoreZ) {
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norm_diff = cutlass::reference::host::TensorNormDiff(tensor_Z_ref.host_view(), tensor_Z.host_view(), float());
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passed = (norm_diff <= 0.1f);
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EXPECT_LT(norm_diff, 0.1f) << " tensor_Z is incorrect";
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}
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if (OutputOp::kStoreT) {
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norm_diff = cutlass::reference::host::TensorNormDiff(tensor_T_ref.host_view(), tensor_T.host_view(), float());
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passed = (passed && (norm_diff <= 0.1f));
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EXPECT_LT(norm_diff, 0.1f) << " tensor_T is incorrect";
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}
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if (!passed) {
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/*
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std::stringstream fname;
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fname << "error_Gemm_device_"
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<< problem_size.m() << "x"
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<< problem_size.n() << "x"
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<< problem_size.k() << "_"
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<< Gemm::ThreadblockShape::kM << "x"
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<< Gemm::ThreadblockShape::kN << "x"
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<< Gemm::ThreadblockShape::kK << "_"
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<< Gemm::WarpShape::kM << "x"
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<< Gemm::WarpShape::kN << "x"
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<< Gemm::WarpShape::kK << ".txt";
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std::ofstream file(fname.str());
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*/
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std::ofstream file("errors_testbed_gemm_with_broadcast.txt");
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file
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<< "problem: " << problem_size
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<< ", alpha: " << alpha << ", beta: " << beta << "\n\n";
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file
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<< "A =\n" << tensor_A.host_view()
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<< "\nB =\n" << tensor_B.host_view()
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<< "\nC =\n" << tensor_C.host_view()
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<< "\nZ =\n" << tensor_Z.host_view()
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<< "\nT =\n" << tensor_T.host_view()
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<< "\n\n"
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<< "\nY_ref =\n" << tensor_Y_ref.host_view()
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<< "\nZ_ref =\n" << tensor_Z_ref.host_view()
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<< "\nT_ref =\n" << tensor_T_ref.host_view();
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}
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return passed;
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}
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/// Verifies the result is a GEMM
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bool verify(
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cutlass::gemm::GemmCoord problem_size,
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ElementAccumulator alpha,
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ElementAccumulator beta) {
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//
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// Verify
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//
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cutlass::reference::host::GemmComplex<
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typename Gemm::ElementA, typename Gemm::LayoutA,
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typename Gemm::ElementB, typename Gemm::LayoutB,
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ElementAccumulator, typename Gemm::LayoutC,
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ElementAccumulator, ElementAccumulator
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>(
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problem_size,
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alpha,
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tensor_A.host_ref(),
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Gemm::kTransformA,
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tensor_B.host_ref(),
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Gemm::kTransformB,
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beta,
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tensor_C_ref.host_ref(),
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tensor_Y_ref.host_ref(),
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ElementAccumulator(0)
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);
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using ElementC = typename Gemm::ElementC;
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ReferenceOp reference_op;
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// compute tensor Z and tensor T
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for (int m = 0; m < problem_size.m(); ++m) {
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for (int n = 0; n < problem_size.n(); ++n) {
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ElementZ z;
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ElementT t;
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reference_op(z, t, tensor_Y_ref.at({m, n}), tensor_Broadcast.at({m, 0}));
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tensor_Z_ref.at({m, n}) = z;
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tensor_T_ref.at({m, n}) = t;
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}
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}
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return compare_reference(problem_size, alpha, beta);
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}
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/// Returns true if the CUDA device is sufficient to execute the kernel.
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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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int smem_size = int(sizeof(typename Gemm::GemmKernel::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.sharedMemPerMultiprocessor < 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::gemm::GemmUniversalMode mode,
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cutlass::gemm::GemmCoord problem_size,
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int batch_count = 1,
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ElementAccumulator alpha = ElementAccumulator(1),
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ElementAccumulator beta = ElementAccumulator(0)) {
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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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this->initialize(problem_size);
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//
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// Initialize the GEMM operator
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//
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typename Gemm::Arguments arguments{
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mode,
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problem_size,
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batch_count,
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{alpha, beta},
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tensor_A.device_data(),
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tensor_B.device_data(),
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tensor_C.device_data(),
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tensor_Z.device_data(),
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tensor_Broadcast.device_data(),
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tensor_T.device_data(),
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problem_size.m() * problem_size.k(),
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problem_size.n() * problem_size.k(),
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problem_size.m() * problem_size.n(),
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problem_size.m() * problem_size.n(),
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problem_size.m(),
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problem_size.m() * problem_size.n(),
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tensor_A.layout().stride(0),
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tensor_B.layout().stride(0),
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tensor_C.layout().stride(0),
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tensor_Z.layout().stride(0),
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0, // This must be zero
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tensor_T.layout().stride(0),
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};
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Gemm gemm_op;
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size_t workspace_size = Gemm::get_workspace_size(arguments);
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cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
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cutlass::Status status = gemm_op.initialize(arguments, workspace.get());
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EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
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//
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// Run the GEMM
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//
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status = gemm_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
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//
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// Verify
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//
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bool passed = true;
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passed = this->verify(problem_size, alpha, beta);
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if (!passed) {
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std::cout << "Failed with batch_count/split_k_slices = " << batch_count << std::endl;
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}
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//
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// Profile
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//
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#if 0 // profiling disabled for now.
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int const kWorkspaces = 100;
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cutlass::DeviceAllocation<typename Gemm::ElementA> profiling_tensor_A(tensor_A.capacity() * kWorkspaces);
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cutlass::DeviceAllocation<typename Gemm::ElementB> profiling_tensor_B(tensor_B.capacity() * kWorkspaces);
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cutlass::DeviceAllocation<ElementC> profiling_tensor_C(tensor_C.capacity() * kWorkspaces);
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cutlass::DeviceAllocation<ElementC> profiling_tensor_Broadcast(tensor_Broadcast.capacity() * kWorkspaces);
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cutlass::DeviceAllocation<ElementZ> profiling_tensor_Z(tensor_Z.capacity() * kWorkspaces);
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cutlass::DeviceAllocation<ElementT> profiling_tensor_T(tensor_T.capacity() * kWorkspaces);
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cudaEvent_t events[2];
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for (auto & event : events) {
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cudaError_t result = cudaEventCreate(&event);
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if (result != cudaSuccess) {
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EXPECT_EQ(result, cudaSuccess) << " cudaEventCreate() failed with error " << cudaGetErrorString(result);
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return false;
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break;
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}
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}
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int const kWarmupIterations = 5;
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int const kProfilingIterations = 100;
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for (int i = 0; i < kWarmupIterations; ++i) {
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status = gemm_op();
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EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
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}
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cudaError_t result = cudaEventRecord(events[0]);
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EXPECT_EQ(result, cudaSuccess);
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for (int i = 0; i < kProfilingIterations; ++i) {
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|
|
|
typename Gemm::Arguments arguments{
|
|
mode,
|
|
problem_size,
|
|
batch_count,
|
|
{alpha, beta},
|
|
profiling_tensor_A.get() + tensor_A.capacity() * (i % kWorkspaces),
|
|
profiling_tensor_B.get() + tensor_B.capacity() * (i % kWorkspaces),
|
|
profiling_tensor_C.get() + tensor_C.capacity() * (i % kWorkspaces),
|
|
profiling_tensor_Z.get() + tensor_Z.capacity() * (i % kWorkspaces),
|
|
profiling_tensor_Broadcast.get() + tensor_Broadcast.capacity() * (i % kWorkspaces),
|
|
profiling_tensor_T.get() + tensor_T.capacity() * (i % kWorkspaces),
|
|
problem_size.m() * problem_size.k(),
|
|
problem_size.n() * problem_size.k(),
|
|
problem_size.m() * problem_size.n(),
|
|
problem_size.m() * problem_size.n(),
|
|
problem_size.m(),
|
|
problem_size.m() * problem_size.n(),
|
|
tensor_A.layout().stride(0),
|
|
tensor_B.layout().stride(0),
|
|
tensor_C.layout().stride(0),
|
|
tensor_Z.layout().stride(0),
|
|
0, // This must be zero
|
|
tensor_T.layout().stride(0),
|
|
};
|
|
|
|
gemm_op.initialize(arguments, workspace.get());
|
|
status = gemm_op();
|
|
EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
|
|
}
|
|
|
|
result = cudaEventRecord(events[1]);
|
|
EXPECT_EQ(result, cudaSuccess);
|
|
|
|
result = cudaDeviceSynchronize();
|
|
EXPECT_EQ(result, cudaSuccess);
|
|
|
|
float elapsed_time = 0;
|
|
result = cudaEventElapsedTime(&elapsed_time, events[0], events[1]);
|
|
EXPECT_EQ(result, cudaSuccess);
|
|
|
|
double average_time = double(elapsed_time) / double(kProfilingIterations);
|
|
|
|
std::cout << problem_size << ": " << average_time << " ms" << std::endl;
|
|
|
|
for (auto & event : events) {
|
|
cudaEventDestroy(event);
|
|
}
|
|
#endif
|
|
|
|
return passed;
|
|
}
|
|
};
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename Gemm,
|
|
typename ReferenceOp = GemmWithBroadcastReferenceOp<Gemm>
|
|
>
|
|
bool TestGemmWithBroadcast(
|
|
cutlass::gemm::GemmCoord const & problem_size,
|
|
cutlass::gemm::GemmUniversalMode mode,
|
|
int batch_count,
|
|
double alpha = 1.0,
|
|
double beta = 2.0) {
|
|
|
|
bool passed = true;
|
|
|
|
TestbedGemmWithBroadcast<Gemm, ReferenceOp> testbed;
|
|
|
|
using ElementAccumulator = typename Gemm::ElementAccumulator;
|
|
|
|
passed = testbed.run(
|
|
mode,
|
|
problem_size,
|
|
batch_count,
|
|
cutlass::from_real<ElementAccumulator>(alpha),
|
|
cutlass::from_real<ElementAccumulator>(beta)
|
|
);
|
|
|
|
return passed;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
template <
|
|
typename Gemm,
|
|
typename ReferenceOp = GemmWithBroadcastReferenceOp<Gemm>
|
|
>
|
|
bool TestAllGemmWithBroadcast() {
|
|
|
|
int M_problems[] = {8, 136, 264, 520};
|
|
int N_problems[] = {8, 136, 264, 520};
|
|
int K_problems[] = {8, 136, 264, 520};
|
|
double alpha_problems[] = {1.25, 2.25};
|
|
double beta_problems[] = {0, 1, 2.0};
|
|
|
|
bool passed = true;
|
|
|
|
for (int M : M_problems) {
|
|
for (int N : N_problems) {
|
|
for (int K : K_problems) {
|
|
for (double alpha : alpha_problems) {
|
|
for (double beta : beta_problems) {
|
|
|
|
TestbedGemmWithBroadcast<Gemm, ReferenceOp> testbed;
|
|
|
|
using ElementAccumulator = typename Gemm::ElementAccumulator;
|
|
|
|
passed = testbed.run(
|
|
cutlass::gemm::GemmUniversalMode::kGemm,
|
|
{M, N, K},
|
|
1,
|
|
cutlass::from_real<ElementAccumulator>(alpha),
|
|
cutlass::from_real<ElementAccumulator>(beta)
|
|
);
|
|
|
|
EXPECT_TRUE(passed)
|
|
<< "M: " << M << ", N: " << N << ", K: " << K << ", alpha: " << alpha << ", beta: " << beta;
|
|
|
|
if (!passed) {
|
|
|
|
return passed;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return passed;
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
} // namespace device
|
|
} // namespace gemm
|
|
} // namespace test
|
|
|
|
/////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|