Spaces:
Runtime error
Runtime error
File size: 8,286 Bytes
d1a84ee |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 |
// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "sparse_matmul/layers/sparse_linear_layer.h"
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "sparse_matmul/numerics/test_utils.h"
namespace csrblocksparse {
namespace {
constexpr int kBlockSize = 4;
constexpr int kSize = 256;
constexpr int kNumThreads = 4;
constexpr int kCols = 1;
void SlicedThreadBody(SpinBarrier* spin_barrier, int tid,
const FatCacheAlignedVector<float>& rhs,
SparseLinearLayer<float, float>* sparse_linear_layer,
FatCacheAlignedVector<float>* out, bool use_relu) {
sparse_linear_layer->MatVec(rhs, use_relu, tid, /*replicas=*/1,
/*output_stride=*/0, out);
spin_barrier->barrier();
}
// Tests that a Layer that has been SliceForThreads computes the same result as
// the original layer. This is a basic test that all the slicing didn't mess up
// any of the computations.
TEST(CsrBlockSparseMatrix, SliceForThreads) {
MaskedSparseMatrix<float> matrix(kSize, kSize, 0.95, kBlockSize, kBlockSize);
FatCacheAlignedVector<float> rhs(kSize, kCols);
CacheAlignedVector<float> bias(kSize);
FatCacheAlignedVector<float> out1(kSize, kCols);
bias.FillRandom();
rhs.FillRandom();
out1.FillZero();
FatCacheAlignedVector<float> out_reference = out1;
CsrBlockSparseMatrix<float, float> sparse_matrix(matrix);
SparseLinearLayer<float, float> sparse_linear_layer(std::move(sparse_matrix),
std::move(bias));
sparse_linear_layer.PrepareForThreads(1);
sparse_linear_layer.MatVec(rhs, /*relu=*/true, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out_reference);
std::vector<int> fake_split_points = {0, 48 / kBlockSize, 128 / kBlockSize,
208 / kBlockSize, kSize / kBlockSize};
sparse_linear_layer.PrepareForThreads(kNumThreads);
sparse_linear_layer.SliceForThreads(fake_split_points);
csrblocksparse::LaunchOnThreadsWithBarrier(kNumThreads, SlicedThreadBody, rhs,
&sparse_linear_layer, &out1,
/*relu=*/true);
CheckResult(out_reference, out1, kCols);
}
void LayersThreadBody(SpinBarrier* spin_barrier, int tid,
const FatCacheAlignedVector<float>& rhs,
SparseLinearLayer<float, float>* sparse_linear_layer1,
SparseLinearLayer<float, float>* sparse_linear_layer2,
FatCacheAlignedVector<float>* out1,
FatCacheAlignedVector<float>* out2, bool use_relu) {
sparse_linear_layer1->MatVec(rhs, use_relu, tid, /*replicas=*/1,
/*output_stride=*/0, out1);
// NOTE no barrier here!
sparse_linear_layer2->MatVec(*out1, use_relu, tid, /*replicas=*/1,
/*output_stride=*/0, out2);
spin_barrier->barrier();
}
// Tests that a pair of layers computes the same result whether or not the
// second layer has been SliceForThreads. This is a more critical test that
// the replacement of barriers with producer-consumer locks works.
// Must be run with tsan to really test it properly.
TEST(CsrBlockSparseMatrix, SliceForThreadsLayers) {
MaskedSparseMatrix<float> matrix1(kSize, kSize, 0.95, kBlockSize, kBlockSize);
FatCacheAlignedVector<float> rhs(kSize, kCols);
CacheAlignedVector<float> bias1(kSize);
FatCacheAlignedVector<float> out1(kSize, kCols);
MaskedSparseMatrix<float> matrix2(kSize, kSize, 0.95, kBlockSize, kBlockSize);
CacheAlignedVector<float> bias2(kSize);
FatCacheAlignedVector<float> out2(kSize, kCols);
bias1.FillRandom();
rhs.FillRandom();
bias2.FillRandom();
out1.FillZero();
out2.FillZero();
FatCacheAlignedVector<float> out_reference = out2;
CsrBlockSparseMatrix<float, float> sparse_matrix1(matrix1);
SparseLinearLayer<float, float> layer1(std::move(sparse_matrix1),
std::move(bias1));
CsrBlockSparseMatrix<float, float> sparse_matrix2(matrix2);
SparseLinearLayer<float, float> layer2(std::move(sparse_matrix2),
std::move(bias2));
layer1.PrepareForThreads(1);
layer2.PrepareForThreads(1);
layer1.MatVec(rhs, /*relu=*/true, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out1);
layer2.MatVec(out1, /*relu=*/true, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out_reference);
layer1.PrepareForThreads(kNumThreads);
layer2.PrepareForThreads(kNumThreads);
layer2.SliceForThreads(layer1.split_points());
csrblocksparse::LaunchOnThreadsWithBarrier(kNumThreads, LayersThreadBody, rhs,
&layer1, &layer2, &out1, &out2,
/*relu=*/true);
CheckResult(out_reference, out2, kCols);
}
// Tests that a Layer that has been DoubleBlockHeight()-ed computes the same
// result as original layer. (Float compute type).
TEST(CsrBlockSparseMatrix, Float8x4) {
using ComputeType = float;
using RhsType = float;
using BiasType = float;
MaskedSparseMatrix<float> matrix(kSize, kSize, 0.95, kBlockSize, kBlockSize);
matrix.CastWeights<ComputeType>();
FatCacheAlignedVector<RhsType> rhs(kSize, kCols);
CacheAlignedVector<BiasType> bias(kSize);
FatCacheAlignedVector<BiasType> out1(kSize, kCols);
bias.FillRandom();
rhs.FillRandom();
out1.FillZero();
FatCacheAlignedVector<BiasType> out_reference = out1;
CsrBlockSparseMatrix<ComputeType, RhsType> sparse_matrix(matrix);
SparseLinearLayer<ComputeType, RhsType> sparse_linear_layer(
std::move(sparse_matrix), std::move(bias));
sparse_linear_layer.PrepareForThreads(1);
sparse_linear_layer.MatVec(rhs, /*relu=*/true, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out_reference);
sparse_linear_layer.DoubleBlockHeight();
sparse_linear_layer.PrepareForThreads(1);
sparse_linear_layer.MatVec(rhs, /*relu=*/true, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out1);
CheckResult(out_reference, out1, kCols);
}
// Tests that a Layer that has been DoubleBlockHeight()-ed computes the same
// result as original layer. (Fixed16 compute type).
TEST(CsrBlockSparseMatrix, Fixed8x4) {
using ComputeType = csrblocksparse::fixed16<4>;
using RhsType = csrblocksparse::fixed16<4>;
using BiasType = typename TypeOfProduct<ComputeType, RhsType>::type;
MaskedSparseMatrix<float> matrix(kSize, kSize, 0.95, kBlockSize, kBlockSize);
matrix.CastWeights<ComputeType>();
FatCacheAlignedVector<RhsType> rhs(kSize, kCols);
CacheAlignedVector<BiasType> bias(kSize);
FatCacheAlignedVector<BiasType> out1(kSize, kCols);
bias.FillRandom();
rhs.FillRandom();
out1.FillZero();
FatCacheAlignedVector<BiasType> out_reference = out1;
CsrBlockSparseMatrix<ComputeType, RhsType> sparse_matrix(matrix);
SparseLinearLayer<ComputeType, RhsType> sparse_linear_layer(
std::move(sparse_matrix), std::move(bias));
sparse_linear_layer.PrepareForThreads(1);
sparse_linear_layer.MatVec(rhs, /*relu=*/false, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out_reference);
sparse_linear_layer.DoubleBlockHeight();
sparse_linear_layer.PrepareForThreads(1);
sparse_linear_layer.MatVec(rhs, /*relu=*/false, /*tid=*/0, /*replicas=*/1,
/*output_stride=*/0, &out1);
CheckResult(out_reference, out1, kCols);
}
TEST(SparseLinearLayerTest, PrintCompiles) {
SparseLinearLayer<float, float> sparse_linear_layer;
sparse_linear_layer.Print();
}
} // namespace
} // namespace csrblocksparse
|