leavelet/cutlass
1
0
Fork 0
Code Issues Pull Requests Actions 28 Packages Projects Releases Wiki Activity

CUTLASS 2.2 (#96)

Browse Source 
Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
This commit is contained in:
Andrew Kerr
2020-06-08 16:17:35 -07:00
committed by GitHub
parent e33d90b361
commit 86931fef85
584 changed files with 51080 additions and 3373 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/unit/gemm/threadblock/CMakeLists.txt
+1 -1
View File
@@ -1,4 +1,4 @@
# Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/batched_gemv.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/epilogue_workspace.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_pipelined_simt.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_pipelined_sm70.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_pipelined_sm75.cu
+336 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
@@ -231,6 +231,7 @@ TEST(SM75_gemm_threadblock_congruous,
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_64x64x32_64x64x32_16x8x8) {
using ElementA = cutlass::half_t;
using LayoutA = cutlass::layout::RowMajor;
@@ -562,6 +563,7 @@ TEST(SM75_gemm_threadblock_crosswise,
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_interleaved, tensor_op_32x32x64_16x16x64_8x8x16) {
using ElementA = uint8_t;
using LayoutA = cutlass::layout::ColumnMajorInterleaved<32>;
@@ -1785,4 +1787,337 @@ TEST(SM75_gemm_threadblock_interleaved,
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_64x64x512_64x64x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(64, 64, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 512>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 1, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_32x32x512_16x16x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(32, 32, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<32, 32, 512>;
using WarpShape = cutlass::gemm::GemmShape<16, 16, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_64x32x512_32x16x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(64, 32, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<64, 32, 512>;
using WarpShape = cutlass::gemm::GemmShape<32, 16, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_32x64x512_16x32x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(32, 64, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<32, 64, 512>;
using WarpShape = cutlass::gemm::GemmShape<16, 32, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_64x64x512_32x32x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(64, 64, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<64, 64, 512>;
using WarpShape = cutlass::gemm::GemmShape<32, 32, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_128x64x512_64x32x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(128, 64, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<128, 64, 512>;
using WarpShape = cutlass::gemm::GemmShape<64, 32, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore component
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_64x128x512_32x64x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(64, 128, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<64, 128, 512>;
using WarpShape = cutlass::gemm::GemmShape<32, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise, tensor_op_128x128x512_64x64x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(128, 128, 2048);
using ThreadBlockShape = cutlass::gemm::GemmShape<128, 128, 512>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(1, 1);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise,
multicta_256x256x1536_128x128x512_64x64x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(256, 256, 1536);
using ThreadBlockShape = cutlass::gemm::GemmShape<128, 128, 512>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(2, 2);
dim3 block(32, 4, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
TEST(SM75_gemm_threadblock_crosswise,
multicta_512x256x6144_256x128x512_64x64x512_8x8x128) {
using ElementA = cutlass::uint1b_t;
using LayoutA = cutlass::layout::RowMajor;
using ElementB = cutlass::uint1b_t;
using LayoutB = cutlass::layout::ColumnMajor;
using ElementC = int32_t;
using LayoutC = cutlass::layout::ColumnMajor;
cutlass::gemm::GemmCoord problem_size(512, 256, 6144);
using ThreadBlockShape = cutlass::gemm::GemmShape<256, 128, 512>;
using WarpShape = cutlass::gemm::GemmShape<64, 64, 512>;
using InstructionShape = cutlass::gemm::GemmShape<8, 8, 128>;
float alpha = 1.f;
float beta = 0.f;
// Define the MmaCore components
using MmaCore = typename cutlass::gemm::threadblock::DefaultMmaCore<
ThreadBlockShape, WarpShape, InstructionShape, ElementA, LayoutA,
ElementB, LayoutB, ElementC, LayoutC, cutlass::arch::OpClassTensorOp, 2,
cutlass::arch::OpXorPopc>;
dim3 grid(2, 2);
dim3 block(32, 8, 1);
test::gemm::threadblock::Testbed<MmaCore>(problem_size.m(), problem_size.n(),
problem_size.k(), alpha, beta)
.run(grid, block);
}
////////////////////////////////////////////////////////////////////////////////
#endif
test/unit/gemm/threadblock/mma_pipelined_testbed.h
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_pipelined_wmma_sm70.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_pipelined_wmma_sm75.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_planar_complex_testbed.h
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_singlestage_wmma_sm70.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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:
test/unit/gemm/threadblock/mma_singlestage_wmma_sm75.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2020, 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: