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v4.0 update. (#2371)

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Junkai-Wu
2025-06-06 02:39:20 -04:00
committed by GitHub
parent 2e2af190bd
commit 8bdbfca682
254 changed files with 29751 additions and 1980 deletions
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test/unit/cute/core/CMakeLists.txt
+1 -1
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@@ -50,6 +50,6 @@ cutlass_test_unit_add_executable(
pointer.cpp
reverse.cpp
swizzle_layout.cpp
transform.cpp
tensor_algs.cpp
tuple.cpp
)
test/unit/cute/core/tensor_algs.cpp
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@@ -0,0 +1,200 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#include "cutlass_unit_test.h"
#include <cute/algorithm/tensor_algorithms.hpp>
#include <cute/algorithm/tensor_reduce.hpp>
#include <cute/numeric/complex.hpp>
TEST(CuTe_algorithm, TensorTransform) {
using namespace cute;
complex<float> array[4] = {{0,0}, {1,0}, {0,1}, {1,1}};
complex<float> correct[4] = {{0,0}, {1,0}, {0,-1}, {1,-1}};
Tensor tensor = make_tensor(static_cast<complex<float>*>(array), make_layout(make_shape(4)));
conjugate conj;
transform(tensor, conj);
for (int i = 0; i < 4; ++i) {
EXPECT_EQ(tensor(i), correct[i]);
}
}
TEST(CuTe_algorithm, TensorBatchReduce) {
using namespace cute;
int src_vals[16] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
Tensor src_tensor = make_tensor(static_cast<int*>(src_vals),
make_layout(make_shape (make_shape (2,2), make_shape (2,2)),
make_stride(make_stride(2,8), make_stride(1,4))));
array<int, 4> dst_vals;
fill(dst_vals, 0);
Tensor dst_tensor = make_tensor(dst_vals.begin(), make_shape(2,2));
batch_reduce(src_tensor, dst_tensor);
int correct[4] = {20,24,36,40};
for (int i = 0; i < 4; ++i) {
//printf("%d %d\n", dst_tensor(i), correct[i]);
EXPECT_EQ(dst_tensor(i), correct[i]);
}
}
TEST(CuTe_algorithm, TensorLogicalReduce) {
using namespace cute;
{ // Reduce each column of a matrix
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape <_32, Shape <_12,_6>>,
Stride< _1, Stride<_64,_1>>>{});
auto slicer = make_coord(0_c, _);
Tensor dst_tensor = make_tensor_like(src_tensor(slicer));
logical_reduce(src_tensor, dst_tensor, slicer);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), reduce(src_tensor(_,i), int(0)));
}
}
{ // Reduce each row of a matrix
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape <_32, Shape <_12,_6>>,
Stride< _1, Stride<_64,_1>>>{});
auto slicer = make_coord(_, 0_c);
Tensor dst_tensor = make_tensor_like(src_tensor(slicer));
logical_reduce(src_tensor, dst_tensor, slicer);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), reduce(src_tensor(i,_), int(0)));
}
}
{ // 1 profile
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape<_32>, Stride<_1>>{});
array<int, 1> dst_vals;
fill(dst_vals, 0);
Tensor dst_tensor = make_tensor(dst_vals.begin(), Layout<_1,_0>{});
logical_reduce(src_tensor, dst_tensor, 1);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), reduce(src_tensor, int(0)));
}
}
{ // _ profile
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape<_32>, Stride<_1>>{});
auto slicer = _;
Tensor dst_tensor = make_tensor_like(src_tensor(slicer));
logical_reduce(src_tensor, dst_tensor, slicer);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), src_tensor(i));
}
}
{ // (1,1) profile
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape <_32, Shape <_12,_6>>,
Stride< _1, Stride<_192,_32>>>{});
auto slicer = make_coord(1, 1);
array<int, 1> dst_vals;
fill(dst_vals, 0);
Tensor dst_tensor = make_tensor(dst_vals.begin(), Layout<_1,_0>{});
logical_reduce(src_tensor, dst_tensor, slicer);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), reduce(src_tensor, int(0)));
}
}
{ // (_,_) profile
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
Layout<Shape <_32, Shape <_12,_6>>,
Stride< _1, Stride<_192,_32>>>{});
auto slicer = make_coord(_,_);
Tensor dst_tensor = make_tensor_like(src_tensor(slicer));
logical_reduce(src_tensor, dst_tensor, slicer);
for (int i = 0; i < size(dst_tensor); ++i) {
EXPECT_EQ(dst_tensor(i), src_tensor(i));
}
}
{
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
make_layout(make_shape (2,2,2,2),
make_stride(1,2,4,8)));
array<int, 4> dst_vals;
fill(dst_vals, 0);
Tensor dst_tensor = make_tensor(dst_vals.begin(), make_shape(2,2));
auto target_profile = make_coord(_,1,_,1);
logical_reduce(src_tensor, dst_tensor, target_profile);
int correct[4] = {20,24,36,40};
for (int i = 0; i < 4; ++i) {
//printf("%d %d\n", dst_tensor(i), correct[i]);
EXPECT_EQ(dst_tensor(i), correct[i]);
}
}
{
Tensor src_tensor = make_tensor(counting_iterator<int>{0},
make_layout(make_shape (2,make_shape (2,2),2),
make_stride(1,make_stride(2,4),8)));
array<int, 4> dst_vals;
fill(dst_vals, 0);
Tensor dst_tensor = make_tensor(dst_vals.begin(), make_shape(2,2));
auto target_profile = make_coord(_,make_coord(1,_),1);
logical_reduce(src_tensor, dst_tensor, target_profile);
int correct[4] = {20,24,36,40};
for (int i = 0; i < 4; ++i) {
//printf("%d %d\n", dst_tensor(i), correct[i]);
EXPECT_EQ(dst_tensor(i), correct[i]);
}
}
}
test/unit/cute/core/transform.cpp
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@@ -1,49 +0,0 @@
/***************************************************************************************************
* Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. 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.
*
* 3. Neither the name of the copyright holder 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 THE COPYRIGHT HOLDER OR CONTRIBUTORS 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 TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
#include "cutlass_unit_test.h"
#include <cutlass/trace.h>
#include <cute/tensor.hpp>
#include <cute/numeric/complex.hpp>
TEST(CuTe_core, Transform) {
using namespace cute;
complex<float> array[4] = {{0,0}, {1,0}, {0,1}, {1,1}};
complex<float> correct[4] = {{0,0}, {1,0}, {0,-1}, {1,-1}};
auto tensor = make_tensor(static_cast<complex<float>*>(array), make_layout(make_shape(4)));
conjugate conj;
transform(tensor, conj);
for (int i = 0; i < 4; ++i)
{
EXPECT_EQ(tensor(i), correct[i]);
}
}