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New updates for 2.11 (#775)

Browse Source 
* New updates.

* Minor profiler updates

Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
This commit is contained in:
ANIKET SHIVAM
2023-01-20 16:32:57 -05:00
committed by GitHub
co-authored by Aniket Shivam
parent d49bef88f9
commit 66d9cddc83
1341 changed files with 2003 additions and 1732 deletions
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test/unit/gemm/threadblock/CMakeLists.txt
+1 -1
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@@ -1,4 +1,4 @@
# Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/batched_gemv.cu
+102 -102
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@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
@@ -107,8 +107,8 @@ template<typename Shape_,
typename LayoutA_,
typename LayoutB_,
typename LayoutC_,
int LDG_N,
int LDG_K,
int THREAD_N,
int THREAD_K,
int MAX_THREADS_PER_BLOCK=512,
bool DEBUG=false>
void batched_gemv_threadblock_test(cutlass::gemm::GemmCoord problem_size, int num_batch)
@@ -120,7 +120,7 @@ void batched_gemv_threadblock_test(cutlass::gemm::GemmCoord problem_size, int nu
using LayoutB = LayoutB_;
using ElementC = ElementC_;
using LayoutC = LayoutC_;
using ThreadShape = cutlass::gemm::GemmShape<1, LDG_N, LDG_K>;
using ThreadShape = cutlass::gemm::GemmShape<1, THREAD_N, THREAD_K>;
using Core = typename cutlass::gemm::threadblock::DefaultGemvCore<
Shape,
@@ -192,14 +192,14 @@ void batched_gemv_threadblock_test(cutlass::gemm::GemmCoord problem_size, int nu
matrix_C_computed.sync_device();
dim3 grid(1, 1); // only 1 CTA is used
dim3 block(Shape::kN / LDG_N, num_batch, 1);
dim3 block(Shape::kN / THREAD_N, num_batch, 1);
#if 0
printf("block dim = %d x %d\n", block.x, block.y);
#endif
// Some sanity checks
EXPECT_TRUE( problem_size.n() % LDG_N == 0 );
EXPECT_TRUE( problem_size.n() % THREAD_N == 0 );
EXPECT_TRUE( block.x*block.y <= MAX_THREADS_PER_BLOCK );
test::gemm::threadblock::batched_gemv_threadblock_test_kernel<Mma><<< grid, block >>>(
@@ -261,126 +261,126 @@ TEST(SM50_batched_gemv_threadblock, 4x1x64x64_crc_fp32_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 5x1x128x128_crc_fp32_fp32_4N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 128, 128);
const int num_batch = 5;
const int LDG_N = 4;
const int LDG_K = 4;
const int THREAD_N = 4;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_crc_fp32_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
float, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_crc_fp16_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_crc_fp16_fp32_2N_8K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 8;
const int THREAD_N = 2;
const int THREAD_K = 8;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_crc_fp16_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_crc_i8_i32_2N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 4;
const int THREAD_N = 2;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_crc_i8_i32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
// A: RowMajor
@@ -392,126 +392,126 @@ TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcr_fp32_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 5x1x128x128_rcr_fp32_fp32_4N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 128, 128);
const int num_batch = 5;
const int LDG_N = 4;
const int LDG_K = 4;
const int THREAD_N = 4;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcr_fp32_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcr_fp16_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcr_fp16_fp32_2N_8K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 8;
const int THREAD_N = 2;
const int THREAD_K = 8;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcr_fp16_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcr_i8_i32_2N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 4;
const int THREAD_N = 2;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcr_i8_i32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::RowMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
// A: RowMajor
@@ -523,124 +523,124 @@ TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcc_fp32_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 5x1x128x128_rcc_fp32_fp32_4N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 128, 128);
const int num_batch = 5;
const int LDG_N = 4;
const int LDG_K = 4;
const int THREAD_N = 4;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape, float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcc_fp32_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
float, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcc_fp16_fp32_2N_2K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 2;
const int THREAD_N = 2;
const int THREAD_K = 2;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcc_fp16_fp32_2N_8K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 8;
const int THREAD_N = 2;
const int THREAD_K = 8;
using Shape = cutlass::gemm::GemmShape<1, 64, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 64, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcc_fp16_fp32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
cutlass::half_t, float,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 4x1x64x64_rcc_i8_i32_2N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 64, 64);
const int num_batch = 4;
const int LDG_N = 2;
const int LDG_K = 4;
const int THREAD_N = 2;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 128, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 128, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
TEST(SM50_batched_gemv_threadblock, 16x1x17x64_rcc_i8_i32_1N_4K) {
using namespace test::gemm::threadblock;
cutlass::gemm::GemmCoord problem_size(1, 17, 64);
const int num_batch = 16;
const int LDG_N = 1;
const int LDG_K = 4;
const int THREAD_N = 1;
const int THREAD_K = 4;
using Shape = cutlass::gemm::GemmShape<1, 32, LDG_K>;
using Shape = cutlass::gemm::GemmShape<1, 32, THREAD_K>;
batched_gemv_threadblock_test<Shape,
int8_t, int32_t,
cutlass::layout::RowMajor,
cutlass::layout::ColumnMajor,
cutlass::layout::ColumnMajor,
LDG_N, LDG_K>(problem_size, num_batch);
THREAD_N, THREAD_K>(problem_size, num_batch);
}
test/unit/gemm/threadblock/epilogue_workspace.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage_slicedk.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage_sparse.cu
+1 -1
View File
@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage_sparse_testbed.h
+1 -1
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage_testbed.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_multistage_testbed_slicedk.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_simt.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_slicedk.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_sm70.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_sm75.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_sm80.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_testbed.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_testbed_slicedk.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_wmma_sm70.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_pipelined_wmma_sm75.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_planar_complex_sm80.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_planar_complex_testbed.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_singlestage_wmma_sm70.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
test/unit/gemm/threadblock/mma_singlestage_wmma_sm75.cu
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without