354 lines
12 KiB
Plaintext
354 lines
12 KiB
Plaintext
/***************************************************************************************************
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* Copyright (c) 2017-2018, 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 TOR (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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/*
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This example demonstrates how to call a CUTLASS GEMM kernel using Turing integer WMMA.
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The CUTLASS integer WMMA Gemm template is instantiated in the function Cutlass_S8_WmmagemmNN. This
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is kernel computes the general matrix product (GEMM) using integer arithmetic accelerated by Turing
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WMMA and assumes all matrices have column-major layout.
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The threadblock tile size is chosen as 128x128x8 which offers good performance for large matrices.
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See the CUTLASS Parallel for All blog post for more exposition on the tunable parameters available
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in CUTLASS.
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https://devblogs.nvidia.com/cutlass-linear-algebra-cuda/
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This example uses CUTLASS utilities to ease the matrix operations.
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*/
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// Standard Library includes
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#include <iostream>
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#include <sstream>
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#include <vector>
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// CUTLASS includes needed for WMMA GEMM kernel
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#include "cutlass/wmma_matrix.h"
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// This example works only when this MACRO is defined in "cutlass/wmma_matrix.h"
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#ifdef CUTLASS_USE_SUBBYTE_WMMA
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// Defines cutlass::gemm::Gemm, the generic Gemm computation template class.
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#include "cutlass/gemm/gemm.h"
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// Defines cutlass::gemm::WmmaGemmTraits, the structural components for WMMA GEMM
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#include "cutlass/gemm/wmma_gemm_traits.h"
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//
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// CUTLASS utility includes
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//
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// Defines operator<<() to write TensorView objects to std::ostream
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#include "tools/util/tensor_view_io.h"
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// Defines cutlass::HostMatrix<>
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#include "tools/util/host_matrix.h"
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// Defines cutlass::reference::device::TensorInitialize()
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#include "tools/util/reference/device/tensor_elementwise.h"
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// Defines cutlass::reference::host::TensorEquals()
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#include "tools/util/reference/host/tensor_elementwise.h"
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// Defines cutlass::reference::host::Gemm()
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#include "tools/util/reference/host/gemm.h"
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///////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// This function defines a CUTLASS GEMM kernel instantiation, constructs its parameters object,
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// and launches it on the CUDA device.
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//
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///////////////////////////////////////////////////////////////////////////////////////////////////
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/// Define a CUTLASS GEMM template and launch a GEMM kernel.
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cudaError_t Cutlass_S8_WmmagemmNN(
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int M,
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int N,
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int K,
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int alpha,
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signed char const *A,
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int lda,
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signed char const *B,
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int ldb,
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int beta,
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int *C,
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int ldc) {
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// Define type definition for 8-bit signed int WMMA CUTLASS GEMM with column-major
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// input matrices and 128x128x128 threadblock tile size.
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//
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// Note, A and B are 8-bit signed int. C and D are 32-bit int. .
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//
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typedef cutlass::gemm::WmmaGemmTraits<
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cutlass::MatrixLayout::kColumnMajor, // layout of A matrix
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cutlass::MatrixLayout::kColumnMajor, // layout of B matrix
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cutlass::Shape<128, 128, 128>, // threadblock tile size
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signed char, // A type
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signed char, // B type
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int, // D type
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cutlass::gemm::LinearScaling<int>, // functor to do the math in the epilogue
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int, // accumulator type
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cutlass::Shape<128, 32, 32>, // warp tile size
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cutlass::Shape<16, 16, 16>, // WMMA instruction tile size
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16, // scalars every time a thread loads from A
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16 // scalars every time a thread loads from B
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>
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GemmTraits;
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// Define a CUTLASS GEMM type from a GemmTraits<> instantiation.
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typedef cutlass::gemm::Gemm<GemmTraits> Gemm;
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// Construct and initialize CUTLASS GEMM parameters object.
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typename Gemm::Params params;
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int result = params.initialize(
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M, // GEMM M dimension
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N, // GEMM N dimension
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K, // GEMM K dimension
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alpha, // scalar alpha
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A, // matrix A operand
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lda,
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B, // matrix B operand
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ldb,
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beta, // scalar beta
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C, // source matrix C
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ldc,
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C, // destination matrix C (may be different memory than source C matrix)
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ldc
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);
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if (result) {
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std::cerr << "Failed to initialize CUTLASS Gemm::Params object." << std::endl;
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return cudaErrorInvalidValue;
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}
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// Launch the CUTLASS GEMM kernel.
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Gemm::launch(params);
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// Return any errors associated with the launch or cudaSuccess if no error.
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return cudaGetLastError();
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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/// Allocate several matrices in GPU device memory and call an integer
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/// CUTLASS WMMA GEMM kernel.
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cudaError_t TestCutlassGemm(int M, int N, int K, int alpha, int beta) {
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cudaError_t result;
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//
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// Construct cutlass::HostMatrix<> using the integer host-side types.
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// M-by-K matrix of signed char
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cutlass::HostMatrix<signed char> A(cutlass::MatrixCoord(M, K));
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// K-by-N matrix of signed char
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cutlass::HostMatrix<signed char> B(cutlass::MatrixCoord(K, N));
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// M-by-N matrix of int
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cutlass::HostMatrix<int> C_cutlass(cutlass::MatrixCoord(M, N));
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// M-by-N matrix of int
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cutlass::HostMatrix<int> C_reference(cutlass::MatrixCoord(M, N));
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//
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// Initialize matrices with small, random integers.
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//
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cutlass::Distribution dist;
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// Uniform random distribution from -4 .. 4. Values are truncated to integers.
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dist.set_uniform(-4, 4);
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// Arbitrary RNG seed value. Hard-coded for deterministic results.
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int seed = 2080;
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cutlass::reference::device::TensorInitialize(
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A.device_view(), // concept: TensorView
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seed,
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dist);
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cutlass::reference::device::TensorInitialize(
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B.device_view(), // concept: TensorView
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seed * 2,
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dist);
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cutlass::reference::device::TensorInitialize(
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C_cutlass.device_view(), // concept: TensorView
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seed * 3,
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dist);
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// Copy C_cutlass into C_reference so the GEMM is correct when beta != 0.
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cutlass::reference::device::TensorFill(C_reference.device_view(), C_cutlass.device_view());
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// Copy the device-side view into host memory
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C_reference.sync_host();
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//
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// Launch the CUTLASS GEMM kernel
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//
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result = Cutlass_S8_WmmagemmNN(
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M,
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N,
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K,
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alpha,
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A.device_data(),
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A.leading_dim(),
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B.device_data(),
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B.leading_dim(),
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beta,
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C_cutlass.device_data(),
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C_cutlass.leading_dim()
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);
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if (result != cudaSuccess) {
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return result;
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}
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//
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// Verify the result using a host-side reference
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//
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// A and B were initialized using device-side procedures.
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A.sync_host();
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B.sync_host();
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// Copy CUTLASS's GEMM results into host memory.
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C_cutlass.sync_host();
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// Compute the reference result using the host-side GEMM reference implementation.
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cutlass::reference::host::Gemm(
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cutlass::gemm::GemmCoord(K, N, M), // problem size (type: cutlass::gemm::GemmCoord)
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alpha, // alpha (type: int)
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A.host_ref(), // A (concept: TensorRef)
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B.host_ref(), // B (concept: TensorRef)
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beta, // beta (int)
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C_reference.host_ref(), // C (concept: TensorRef)
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int(0) // Accumulator initial value passed as argument to deduce
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); // internal accumulation data type as int.
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// Compare reference to computed results.
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if (!cutlass::reference::host::TensorEquals(C_reference.host_view(), C_cutlass.host_view())) {
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std::cerr << "Error - CUTLASS WMMA GEMM kernel differs from reference." << std::endl;
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//
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// On error, print C_cutlass and C_reference to std::cerr.
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//
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// Result of CUTLASS WMMA GEMM kernel
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std::cerr << "CUTLASS:\n" << C_cutlass << std::endl;
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// Result of reference computation
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std::cerr << "Reference:\n" << C_reference << std::endl;
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// Return error code.
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return cudaErrorUnknown;
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}
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// Passed error check
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return cudaSuccess;
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}
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#endif // defined CUTLASS_USE_SUBBYTE_WMMA
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///////////////////////////////////////////////////////////////////////////////////////////////////
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/// Entry point to wmma_gemm example.
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//
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// usage:
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//
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// 05_wmma_gemm <M> <N> <K> <alpha> <beta>
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//
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int main(int argc, const char *arg[]) {
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#ifdef CUTLASS_USE_SUBBYTE_WMMA
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// Properties of CUDA device
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cudaDeviceProp device_properties;
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// Assumne the device id is 0.
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int device_id = 0;
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cudaError_t result = cudaGetDeviceProperties(&device_properties, device_id);
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if (result != cudaSuccess) {
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std::cerr << "Failed to get device properties: "
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<< cudaGetErrorString(result) << std::endl;
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return -1;
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}
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if ((device_properties.major * 10 + device_properties.minor) < 75) {
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std::cerr << "This example needs to run on a Turing device." << std::endl;
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return -1;
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}
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//
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// Parse the command line to obtain GEMM dimensions and scalar values.
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//
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// GEMM problem dimensions.
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int problem[3] = { 128, 128, 128 };
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for (int i = 1; i < argc && i < 4; ++i) {
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std::stringstream ss(arg[i]);
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ss >> problem[i - 1];
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}
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// Scalars used for linear scaling the result of the matrix product.
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int scalars[2] = { 1, 0 };
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for (int i = 4; i < argc && i < 6; ++i) {
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std::stringstream ss(arg[i]);
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ss >> scalars[i - 4];
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}
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//
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// Run the CUTLASS GEMM test.
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//
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result = TestCutlassGemm(
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problem[0], // GEMM M dimension
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problem[1], // GEMM N dimension
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problem[2], // GEMM K dimension
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scalars[0], // alpha
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scalars[1] // beta
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);
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if (result == cudaSuccess) {
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std::cout << "Passed." << std::endl;
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}
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// Exit.
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return result == cudaSuccess ? 0 : -1;
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#else
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std::cerr << "CUTLASS WMMA GEMM targeting Turing Tensor Cores features requires CUDA 10." << std::endl;
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return -1;
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#endif // defined CUTLASS_USE_SUBBYTE_WMMA
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}
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///////////////////////////////////////////////////////////////////////////////////////////////////
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