CUTLASS 2.0 (#62)
CUTLASS 2.0 Substantially refactored for - Better performance, particularly for native Turing Tensor Cores - Robust and durable templates spanning the design space - Encapsulated functionality embodying modern C++11 programming techniques - Optimized containers and data types for efficient, generic, portable device code Updates to: - Quick start guide - Documentation - Utilities - CUTLASS Profiler Native Turing Tensor Cores - Efficient GEMM kernels targeting Turing Tensor Cores - Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands Coverage of existing CUTLASS functionality: - GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs - Volta Tensor Cores through native mma.sync and through WMMA API - Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions - Batched GEMM operations - Complex-valued GEMMs Note: this commit and all that follow require a host compiler supporting C++11 or greater.
This commit is contained in:
Andrew Kerr
GitHub
26
test/unit/reduction/kernel/CMakeLists.txt
Normal file
26
test/unit/reduction/kernel/CMakeLists.txt
Normal file
@ -0,0 +1,26 @@
|
||||
# Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
# provided that the following conditions are met:
|
||||
# * Redistributions of source code must retain the above copyright notice, this list of
|
||||
# conditions and the following disclaimer.
|
||||
# * 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.
|
||||
# * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
||||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
cutlass_test_unit_add_executable(
|
||||
cutlass_test_unit_reduction_kernel
|
||||
reduce_splitk.cu
|
||||
)
|
||||
383
test/unit/reduction/kernel/reduce_splitk.cu
Normal file
383
test/unit/reduction/kernel/reduce_splitk.cu
Normal file
@ -0,0 +1,383 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
* provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright notice, this list of
|
||||
* conditions and the following disclaimer.
|
||||
* * 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.
|
||||
* * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 TOR (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 Tests for device-wide GEMM interface
|
||||
*/
|
||||
|
||||
#include <iostream>
|
||||
|
||||
#include "../../common/cutlass_unit_test.h"
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
#include "cutlass/reduction/kernel/reduce_split_k.h"
|
||||
#include "cutlass/reduction/thread/reduction_operators.h"
|
||||
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/reference/host/gemm.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_norm.h"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
namespace test {
|
||||
namespace reduction {
|
||||
|
||||
template <typename ReductionKernel>
|
||||
__global__ void kernel_reduce_splitk(typename ReductionKernel::Params params) {
|
||||
|
||||
__shared__ typename ReductionKernel::SharedStorage shared_storage;
|
||||
|
||||
ReductionKernel reduction_op;
|
||||
|
||||
reduction_op(params, shared_storage);
|
||||
}
|
||||
|
||||
template <typename ReductionKernel>
|
||||
class ReduceSplitKTestbed {
|
||||
public:
|
||||
|
||||
using ElementAccumulator = typename ReductionKernel::ElementAccumulator;
|
||||
using ElementWorkspace = typename ReductionKernel::ElementWorkspace;
|
||||
using ElementOutput = typename ReductionKernel::ElementOutput;
|
||||
using Layout = cutlass::layout::RowMajor;
|
||||
|
||||
public:
|
||||
|
||||
cutlass::Distribution::Kind distribution_workspace;
|
||||
cutlass::Distribution::Kind distribution_source;
|
||||
uint64_t seed;
|
||||
|
||||
public:
|
||||
|
||||
/// Ctor
|
||||
ReduceSplitKTestbed(
|
||||
cutlass::Distribution::Kind distribution_workspace = cutlass::Distribution::Uniform,
|
||||
cutlass::Distribution::Kind distribution_source = cutlass::Distribution::Uniform,
|
||||
uint64_t seed = 2019
|
||||
):
|
||||
distribution_workspace(distribution_workspace),
|
||||
distribution_source(distribution_source),
|
||||
seed(seed) {
|
||||
|
||||
}
|
||||
|
||||
/// Helper to initialize a tensor view
|
||||
template <typename Element, typename Layout>
|
||||
bool initialize_tensor(cutlass::TensorView<Element, Layout> view,
|
||||
cutlass::Distribution::Kind dist_kind,
|
||||
uint64_t seed) {
|
||||
|
||||
if (dist_kind == cutlass::Distribution::Uniform) {
|
||||
cutlass::reference::host::TensorFillRandomUniform(view, seed, 8, -8, 0);
|
||||
}
|
||||
else if (dist_kind == cutlass::Distribution::Gaussian) {
|
||||
cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5, -1);
|
||||
} else if (dist_kind == cutlass::Distribution::Identity) {
|
||||
cutlass::reference::host::TensorFillIdentity(view);
|
||||
} else if (dist_kind == cutlass::Distribution::Sequential) {
|
||||
cutlass::reference::host::BlockFillSequential(view.data(),
|
||||
view.capacity());
|
||||
} else {
|
||||
// TODO: Implement the rest
|
||||
EXPECT_TRUE(false) << "Not implemented";
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/// Runs a single problem size
|
||||
bool run(
|
||||
cutlass::MatrixCoord problem_size,
|
||||
int partitions,
|
||||
ElementAccumulator alpha = 1,
|
||||
ElementAccumulator beta = 0) {
|
||||
|
||||
cutlass::HostTensor<ElementWorkspace, Layout> workspace({
|
||||
problem_size.row() * partitions,
|
||||
problem_size.column()
|
||||
});
|
||||
|
||||
cutlass::HostTensor<ElementOutput, Layout> source(problem_size);
|
||||
cutlass::HostTensor<ElementOutput, Layout> destination(problem_size);
|
||||
cutlass::HostTensor<ElementOutput, Layout> destination_reference(problem_size, false);
|
||||
|
||||
//
|
||||
// Initialize
|
||||
//
|
||||
initialize_tensor(workspace.host_view(), distribution_workspace, seed);
|
||||
initialize_tensor(source.host_view(), distribution_source, seed + 23);
|
||||
|
||||
cutlass::reference::host::TensorFill(destination.host_view());
|
||||
|
||||
workspace.sync_device();
|
||||
source.sync_device();
|
||||
destination.sync_device();
|
||||
|
||||
//
|
||||
// Launch reduction kernel
|
||||
//
|
||||
|
||||
dim3 block = ReductionKernel::block_shape();
|
||||
dim3 grid = ReductionKernel::grid_shape(problem_size);
|
||||
|
||||
typename ReductionKernel::Params params(
|
||||
problem_size,
|
||||
partitions,
|
||||
problem_size.row() * problem_size.column(),
|
||||
workspace.device_ref(),
|
||||
destination.device_ref(),
|
||||
source.device_ref(),
|
||||
{alpha, beta}
|
||||
);
|
||||
|
||||
test::reduction::kernel_reduce_splitk<ReductionKernel><<< grid, block >>>(params);
|
||||
|
||||
cudaError_t result = cudaDeviceSynchronize();
|
||||
|
||||
EXPECT_EQ(result, cudaSuccess)
|
||||
<< "CUDA error: " << cudaGetErrorString(result);
|
||||
|
||||
destination.sync_host();
|
||||
|
||||
//
|
||||
// Compute reference
|
||||
//
|
||||
|
||||
for (int m = 0; m < problem_size.row(); ++m) {
|
||||
for (int n = 0; n < problem_size.column(); ++n) {
|
||||
|
||||
ElementAccumulator accum = 0;
|
||||
|
||||
for (int k = 0; k < partitions; ++k) {
|
||||
accum += ElementAccumulator(workspace.at({m + k * problem_size.row(), n}));
|
||||
}
|
||||
|
||||
ElementAccumulator c = ElementAccumulator(source.at({m, n}));
|
||||
|
||||
destination_reference.at({m, n}) = ElementOutput(accum * alpha + beta * c);
|
||||
}
|
||||
}
|
||||
|
||||
//
|
||||
// Compare
|
||||
//
|
||||
|
||||
EXPECT_GT(cutlass::reference::host::TensorNorm(destination.host_view()), 0);
|
||||
EXPECT_GT(cutlass::reference::host::TensorNorm(destination_reference.host_view()), 0);
|
||||
|
||||
bool passed = cutlass::reference::host::TensorEquals(
|
||||
destination.host_view(), destination_reference.host_view());
|
||||
|
||||
EXPECT_TRUE(passed)
|
||||
<< "Workspace =\n" << workspace.host_view() << "\n\n"
|
||||
<< "\n"
|
||||
<< "Reference =\n" << destination_reference.host_view() << "\n\n"
|
||||
<< "Computed =\n" << destination.host_view() << "\n";
|
||||
|
||||
return passed;
|
||||
}
|
||||
|
||||
/// Runs through a variety of test cases
|
||||
bool run_all() {
|
||||
|
||||
cutlass::MatrixCoord problem_sizes[] = {
|
||||
{8, 8},
|
||||
{136, 72},
|
||||
{248, 232},
|
||||
};
|
||||
|
||||
int partition_counts[] = {
|
||||
1,3,4,5,11
|
||||
};
|
||||
|
||||
bool passed = false;
|
||||
|
||||
for (cutlass::MatrixCoord problem : problem_sizes) {
|
||||
for (int partitions : partition_counts) {
|
||||
passed = run(problem, partitions);
|
||||
if (!passed) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return passed;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace reduction
|
||||
} // namespace test
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// Strictly F32 data
|
||||
//
|
||||
TEST(Reduction_ReduceSplitK, f32_f32_f32_1_1x32) {
|
||||
|
||||
using ElementWorkspace = float;
|
||||
using ElementAccumulator = float;
|
||||
using ElementOutput = float;
|
||||
int const kN = 1;
|
||||
using Shape = cutlass::MatrixShape<1, 32>;
|
||||
|
||||
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
||||
ElementOutput,
|
||||
kN,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator
|
||||
>;
|
||||
|
||||
using ReductionOp = cutlass::reduction::thread::ReduceAdd<
|
||||
ElementAccumulator,
|
||||
ElementWorkspace,
|
||||
kN
|
||||
>;
|
||||
|
||||
using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
|
||||
Shape,
|
||||
OutputOp,
|
||||
ReductionOp
|
||||
>;
|
||||
|
||||
test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
|
||||
|
||||
EXPECT_TRUE(testbed.run_all());
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// Vectorized access
|
||||
//
|
||||
TEST(Reduction_ReduceSplitK, f32_f32_f32_2_4x64) {
|
||||
|
||||
using ElementWorkspace = float;
|
||||
using ElementAccumulator = float;
|
||||
using ElementOutput = float;
|
||||
int const kN = 2;
|
||||
using Shape = cutlass::MatrixShape<4, 64>;
|
||||
|
||||
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
||||
ElementOutput,
|
||||
kN,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator
|
||||
>;
|
||||
|
||||
using ReductionOp = cutlass::reduction::thread::ReduceAdd<
|
||||
ElementAccumulator,
|
||||
ElementWorkspace,
|
||||
kN
|
||||
>;
|
||||
|
||||
using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
|
||||
Shape,
|
||||
OutputOp,
|
||||
ReductionOp
|
||||
>;
|
||||
|
||||
test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
|
||||
|
||||
EXPECT_TRUE(testbed.run_all());
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// Vectorized access
|
||||
//
|
||||
TEST(Reduction_ReduceSplitK, f32_f32_f16_2_4x64) {
|
||||
|
||||
using ElementWorkspace = float;
|
||||
using ElementAccumulator = float;
|
||||
using ElementOutput = cutlass::half_t;
|
||||
int const kN = 2;
|
||||
using Shape = cutlass::MatrixShape<4, 64>;
|
||||
|
||||
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
||||
ElementOutput,
|
||||
kN,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator
|
||||
>;
|
||||
|
||||
using ReductionOp = cutlass::reduction::thread::ReduceAdd<
|
||||
ElementAccumulator,
|
||||
ElementWorkspace,
|
||||
kN
|
||||
>;
|
||||
|
||||
using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
|
||||
Shape,
|
||||
OutputOp,
|
||||
ReductionOp
|
||||
>;
|
||||
|
||||
test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
|
||||
|
||||
EXPECT_TRUE(testbed.run_all());
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// Vectorized access
|
||||
//
|
||||
TEST(Reduction_ReduceSplitK, f32_f32_f16_8_4x64) {
|
||||
|
||||
using ElementWorkspace = float;
|
||||
using ElementAccumulator = float;
|
||||
using ElementOutput = cutlass::half_t;
|
||||
int const kN = 8;
|
||||
using Shape = cutlass::MatrixShape<4, 64>;
|
||||
|
||||
using OutputOp = cutlass::epilogue::thread::LinearCombination<
|
||||
ElementOutput,
|
||||
kN,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator
|
||||
>;
|
||||
|
||||
using ReductionOp = cutlass::reduction::thread::ReduceAdd<
|
||||
ElementAccumulator,
|
||||
ElementWorkspace,
|
||||
kN
|
||||
>;
|
||||
|
||||
using ReductionKernel = cutlass::reduction::kernel::ReduceSplitK<
|
||||
Shape,
|
||||
OutputOp,
|
||||
ReductionOp
|
||||
>;
|
||||
|
||||
test::reduction::ReduceSplitKTestbed<ReductionKernel> testbed;
|
||||
|
||||
EXPECT_TRUE(testbed.run_all());
|
||||
}
|
||||
|
||||
/////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
|
||||
39
test/unit/reduction/kernel/reduce_splitk_testbed.h
Normal file
39
test/unit/reduction/kernel/reduce_splitk_testbed.h
Normal file
@ -0,0 +1,39 @@
|
||||
/***************************************************************************************************
|
||||
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without modification, are permitted
|
||||
* provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright notice, this list of
|
||||
* conditions and the following disclaimer.
|
||||
* * 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.
|
||||
* * Neither the name of the NVIDIA CORPORATION 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 NVIDIA CORPORATION 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 TOR (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 Unit tests for thread-level Reduction
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "cutlass/reduction/thread/reduce.h"
|
||||
|
||||
#include "cutlass/layout/vector.h"
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
|
||||
#include "cutlass/util/reference/host/tensor_copy.h"
|
||||
#include "cutlass/util/reference/host/tensor_fill.h"
|
||||
#include "cutlass/util/reference/host/tensor_compare.h"
|
||||
Reference in New Issue
Block a user