Spaces:
Runtime error
Runtime error
File size: 7,713 Bytes
4bdb245 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
// SPDX-License-Identifier: Apache-2.0
#include "gtest/gtest.h"
#include "kompute/Kompute.hpp"
#include "kompute/logger/Logger.hpp"
#include "shaders/Utils.hpp"
TEST(TestOpTensorCopy, CopyDeviceToDeviceTensor)
{
kp::Manager mgr;
std::vector<float> testVecA{ 1, 2, 3 };
std::vector<float> testVecB{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorA = mgr.tensor(testVecA);
std::shared_ptr<kp::TensorT<float>> tensorB = mgr.tensor(testVecB);
EXPECT_TRUE(tensorA->isInit());
EXPECT_TRUE(tensorB->isInit());
mgr.sequence()
->eval<kp::OpTensorSyncDevice>({ tensorA, tensorB })
->eval<kp::OpTensorCopy>({ tensorA, tensorB })
->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB });
// Making sure the GPU holds the same vector
EXPECT_EQ(tensorA->vector(), tensorB->vector());
}
TEST(TestOpTensorCopy, CopyDeviceToDeviceTensorMulti)
{
kp::Manager mgr;
std::vector<float> testVecA{ 2, 3, 4 };
std::vector<float> testVecB{ 0, 0, 0 };
std::vector<float> testVecC{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorA = mgr.tensor(testVecA);
std::shared_ptr<kp::TensorT<float>> tensorB = mgr.tensor(testVecB);
std::shared_ptr<kp::TensorT<float>> tensorC = mgr.tensor(testVecC);
EXPECT_TRUE(tensorA->isInit());
EXPECT_TRUE(tensorB->isInit());
EXPECT_TRUE(tensorC->isInit());
mgr.sequence()
->eval<kp::OpTensorSyncLocal>({ tensorA, tensorB, tensorC })
->eval<kp::OpTensorCopy>({ tensorA, tensorB, tensorC });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
EXPECT_EQ(tensorA->vector(), tensorC->vector());
// Making sure the GPU holds the same vector
mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB, tensorC });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
EXPECT_EQ(tensorA->vector(), tensorC->vector());
}
TEST(TestOpTensorCopy, CopyDeviceToHostTensor)
{
kp::Manager mgr;
std::vector<float> testVecA{ 3, 4, 5 };
std::vector<float> testVecB{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorA = mgr.tensor(testVecA);
std::shared_ptr<kp::TensorT<float>> tensorB =
mgr.tensor(testVecB, kp::Tensor::TensorTypes::eHost);
// Only calling sync on device type tensor
mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA });
EXPECT_TRUE(tensorA->isInit());
EXPECT_TRUE(tensorB->isInit());
mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA, tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
// Making sure the GPU holds the same vector
mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
}
TEST(TestOpTensorCopy, CopyHostToDeviceTensor)
{
kp::Manager mgr;
std::vector<float> testVecA{ 4, 5, 6 };
std::vector<float> testVecB{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorA =
mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::TensorT<float>> tensorB = mgr.tensor(testVecB);
// Only calling sync on device type tensor
mgr.sequence()->eval<kp::OpTensorSyncDevice>({ tensorA, tensorB });
EXPECT_TRUE(tensorA->isInit());
EXPECT_TRUE(tensorB->isInit());
mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA, tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
// Making sure the GPU holds the same vector
mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
}
TEST(TestOpTensorCopy, CopyHostToHostTensor)
{
kp::Manager mgr;
std::vector<float> testVecA{ 5, 6, 7 };
std::vector<float> testVecB{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorA =
mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
std::shared_ptr<kp::TensorT<float>> tensorB =
mgr.tensor(testVecB, kp::Tensor::TensorTypes::eHost);
EXPECT_TRUE(tensorA->isInit());
EXPECT_TRUE(tensorB->isInit());
mgr.sequence()
->eval<kp::OpTensorSyncDevice>({ tensorA })
->eval<kp::OpTensorCopy>({ tensorA, tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
// Making sure the GPU holds the same vector
mgr.sequence()->eval<kp::OpTensorSyncLocal>({ tensorB });
EXPECT_EQ(tensorA->vector(), tensorB->vector());
}
TEST(TestOpTensorCopy, SingleTensorShouldFail)
{
kp::Manager mgr;
std::vector<float> testVecA{ 6, 7, 8 };
std::shared_ptr<kp::TensorT<float>> tensorA =
mgr.tensor(testVecA, kp::Tensor::TensorTypes::eHost);
EXPECT_TRUE(tensorA->isInit());
EXPECT_THROW(mgr.sequence()->eval<kp::OpTensorCopy>({ tensorA }),
std::runtime_error);
}
TEST(TestOpTensorCopy, CopyThroughStorageTensor)
{
kp::Manager mgr;
std::vector<float> testVecIn{ 9, 1, 3 };
std::vector<float> testVecOut{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorIn = mgr.tensor(testVecIn);
std::shared_ptr<kp::TensorT<float>> tensorOut = mgr.tensor(testVecOut);
// Tensor storage requires a vector to be passed only to reflect size
std::shared_ptr<kp::TensorT<float>> tensorStorage =
mgr.tensor({ 0, 0, 0 }, kp::Tensor::TensorTypes::eStorage);
mgr.sequence()
->eval<kp::OpTensorSyncDevice>({ tensorIn, tensorOut })
->eval<kp::OpTensorCopy>({ tensorIn, tensorStorage })
->eval<kp::OpTensorCopy>({ tensorStorage, tensorOut })
->eval<kp::OpTensorSyncLocal>({ tensorIn, tensorOut });
// Making sure the GPU holds the same vector
EXPECT_EQ(tensorIn->vector(), tensorOut->vector());
}
TEST(TestOpTensorCopy, CopyTensorThroughStorageViaAlgorithms)
{
kp::Manager mgr;
std::vector<float> testVecIn{ 9, 1, 3 };
std::vector<float> testVecOut{ 0, 0, 0 };
std::shared_ptr<kp::TensorT<float>> tensorIn = mgr.tensor(testVecIn);
std::shared_ptr<kp::TensorT<float>> tensorOut = mgr.tensor(testVecOut);
// Tensor storage requires a vector to be passed only to reflect size
std::shared_ptr<kp::TensorT<float>> tensorStorage =
mgr.tensor({ 0, 0, 0 }, kp::Tensor::TensorTypes::eStorage);
EXPECT_TRUE(tensorIn->isInit());
EXPECT_TRUE(tensorOut->isInit());
// Copy to storage tensor through algorithm
std::string shaderA = (R"(
#version 450
layout (local_size_x = 1) in;
// The input tensors bind index is relative to index in parameter passed
layout(set = 0, binding = 0) buffer buf_in { float t_in[]; };
layout(set = 0, binding = 1) buffer buf_st { float t_st[]; };
void main() {
uint index = gl_GlobalInvocationID.x;
t_st[index] = t_in[index];
}
)");
auto algoA = mgr.algorithm(
{ tensorIn, tensorStorage },
compileSource(shaderA));
// Copy from storage tensor to output tensor
std::string shaderB = (R"(
#version 450
layout (local_size_x = 1) in;
// The input tensors bind index is relative to index in parameter passed
layout(set = 0, binding = 0) buffer buf_st { float t_st[]; };
layout(set = 0, binding = 1) buffer buf_out { float t_out[]; };
void main() {
uint index = gl_GlobalInvocationID.x;
t_out[index] = t_st[index];
}
)");
auto algoB = mgr.algorithm(
{ tensorStorage, tensorOut },
compileSource(shaderB));
mgr.sequence()
->eval<kp::OpTensorSyncDevice>({ tensorIn })
->eval<kp::OpAlgoDispatch>(algoA)
->eval<kp::OpAlgoDispatch>(algoB)
->eval<kp::OpTensorSyncLocal>({ tensorOut });
// Making sure the GPU holds the same vector
EXPECT_EQ(tensorIn->vector(), tensorOut->vector());
}
|