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
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
|
#include <gtest/gtest.h>
#include <ATen/ATen.h>
#include <ATen/core/interned_strings.h>
#include <ATen/core/ivalue.h>
#include <c10/util/irange.h>
#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/generated/variable_factories.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/codegen/cuda/interface.h>
#include <torch/csrc/jit/codegen/fuser/interface.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/frontend/tracer.h>
#include <torch/csrc/jit/ir/alias_analysis.h>
#include <torch/csrc/jit/ir/attributes.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/common_subexpression_elimination.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/create_autodiff_subgraphs.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/graph_fuser.h>
#include <torch/csrc/jit/passes/lower_grad_of.h>
#include <torch/csrc/jit/passes/lower_tuples.h>
#include <torch/csrc/jit/passes/requires_grad_analysis.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
#include <torch/csrc/jit/runtime/argument_spec.h>
#include <torch/csrc/jit/runtime/autodiff.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <torch/csrc/jit/runtime/interpreter.h>
#include <torch/csrc/jit/runtime/symbolic_script.h>
#include <torch/csrc/jit/serialization/import.h>
#include <torch/csrc/jit/testing/file_check.h>
#include <onnx/onnx_pb.h>
#include <c10/util/Exception.h>
#include <algorithm>
#include <cstddef>
#include <functional>
#include <iostream>
#include <memory>
#include <stdexcept>
#include <string>
#include <tuple>
#include <unordered_set>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
class FuserTest : public ::testing::Test {
void SetUp() override {
old_nvfuser_value_ = fuser::cuda::setEnabled(false);
}
void TearDown() override {
fuser::cuda::setEnabled(old_nvfuser_value_);
}
private:
bool old_nvfuser_value_;
};
TEST_F(FuserTest, TestSimple_CUDA) {
#if defined(FBCODE_CAFFE2)
return;
#endif
const auto graph_string = R"IR(
graph(%0 : Tensor,
%1 : Tensor):
%2 : Tensor = aten::mul(%0, %1)
return (%2))IR";
Graph graph;
torch::jit::parseIR(graph_string, &graph);
auto a = at::rand({3, 4}, at::kCUDA);
auto b = at::rand({4, 3}, at::kCUDA).transpose(0, 1);
auto o = at::zeros({3, 4}, at::kCUDA);
auto outputs = debugLaunchGraph(graph, {a, b});
ASSERT_EQ(outputs.size(), 1);
auto o2 = a * b;
float max_diff = (o2 - outputs[0]).abs().max().item<double>();
// std::cout << "max diff: " << max_diff << "\n";
ASSERT_EQ(max_diff, 0);
}
TEST_F(FuserTest, TestOne_CUDA) {
#if defined(FBCODE_CAFFE2)
return;
#endif
auto testOne = [&](int ti, int tj) {
const auto graph_string = R"IR(
graph(%0 : Tensor,
%1 : Tensor,
%2 : Tensor,
%3 : Tensor,
%4 : Tensor):
%5 : Tensor = aten::sigmoid(%4)
%6 : Tensor = aten::sigmoid(%3)
%7 : Tensor = aten::tanh(%2)
%8 : Tensor = aten::sigmoid(%1)
%9 : Tensor = aten::mul(%6, %0)
%10 : Tensor = aten::mul(%5, %7)
%11 : int = prim::Constant[value=1]()
%12 : Tensor = aten::add(%9, %10, %11)
%13 : Tensor = aten::tanh(%12)
%14 : Tensor = aten::mul(%8, %13)
return (%14, %12))IR";
Graph graph;
torch::jit::parseIR(graph_string, &graph);
graph.lint();
std::vector<at::Tensor> inputs;
// We want to generate input/output tensors with dimension 128x128x32, but
// with different internal strides. To do this, we generate a tensor
// with the "wrong" dimensions, and then use transpose to get an
// appropriately sized view.
std::generate_n(
std::back_inserter(inputs), graph.inputs().size(), [ti, tj] {
std::array<int64_t, 3> dims = {128, 128, 32};
std::swap(dims[ti], dims[tj]);
return at::rand(dims, at::kCUDA).transpose(ti, tj);
});
auto t22 = inputs[4].sigmoid();
auto t20 = inputs[3].sigmoid();
auto t18 = inputs[2].tanh();
auto t16 = inputs[1].sigmoid();
auto t14 = t20 * inputs[0];
auto t11 = t22 * t18;
auto out1 = t14 + t11;
auto t5 = out1.tanh();
auto out0 = t16 * t5;
auto outputs = debugLaunchGraph(graph, inputs);
ASSERT_EQ(outputs.size(), graph.outputs().size());
ASSERT_TRUE(out0.is_same_size(outputs.front()));
float max_diff = (outputs.front() - out0).abs().max().item<double>();
ASSERT_TRUE(max_diff < 1e-6);
};
testOne(0, 0);
testOne(0, 1);
testOne(1, 2);
testOne(0, 2);
}
TEST_F(FuserTest, FusedConcat_CUDA) {
#if defined(FBCODE_CAFFE2)
return;
#endif
const auto graph_string0 = R"IR(
graph(%0 : Tensor,
%1 : Tensor):
%2 : Tensor = aten::mul(%0, %1)
%3 : Tensor = prim::FusedConcat[dim=0](%0, %2)
return (%2, %3))IR";
const auto graph_string1 = R"IR(
graph(%0 : Tensor,
%1 : Tensor):
%2 : Tensor = aten::mul(%0, %1)
%3 : Tensor = prim::FusedConcat[dim=1](%0, %2)
return (%2, %3))IR";
const auto graph_string2 = R"IR(
graph(%0 : Tensor,
%1 : Tensor):
%2 : Tensor = aten::mul(%0, %1)
%3 : Tensor = prim::FusedConcat[dim=2](%0, %2)
return (%2, %3))IR";
auto a = at::rand({3, 4, 5}, at::kCUDA);
auto b = at::rand({4, 3, 5}, at::kCUDA).transpose(0, 1);
const auto o_r = a * b;
std::vector<std::string> graph_strings{
graph_string0, graph_string1, graph_string2};
for (const auto i : c10::irange(graph_strings.size())) {
Graph g;
torch::jit::parseIR(graph_strings[i], &g);
auto outputs = debugLaunchGraph(g, {a, b});
ASSERT_EQ(outputs.size(), 2);
float max_diff = (o_r - outputs[0]).abs().max().item<double>();
ASSERT_EQ(max_diff, 0);
const auto o2_r = at::cat({a, o_r}, i);
float max_diff2 = (o2_r - outputs[1]).abs().max().item<double>();
ASSERT_EQ(max_diff2, 0);
};
}
TEST_F(FuserTest, FusionAliasing) {
#if defined(FBCODE_CAFFE2)
return;
#endif
const auto graph_string = R"IR(
graph(%0 : Tensor,
%1 : Tensor):
%12 : int = prim::Constant[value=1]()
%2.1 : Tensor = aten::mul(%0, %1)
%2 : Tensor = aten::mul(%2.1, %1)
%3 : Tensor = aten::add_(%2, %1, %12)
%4 : Tensor = aten::mul(%2, %1)
%5 : Tensor = aten::add(%2, %4, %12)
return (%5))IR";
auto g = std::make_shared<Graph>();
torch::jit::parseIR(graph_string, g.get());
g->lint();
FuseGraph(g);
// We should not be able to fuse across the in-place operation here.
testing::FileCheck()
.check("prim::FusionGroup_0")
->check("aten::add_")
->check("prim::FusionGroup_1")
->run(*g);
}
TEST_F(FuserTest, KernelCaching) {
#if defined(FBCODE_CAFFE2)
return;
#endif
// Constructs two functionally equivalent graphs
const auto graph0_string = R"IR(
graph(%0 : Float(2, 3, 4),
%1 : Float(2, 3, 4)):
%c0 : Float(2, 3, 4) = aten::mul(%0, %1)
%d0 : Float(2, 3, 4) = aten::mul(%c0, %0)
return (%d0))IR";
auto g0 = std::make_shared<Graph>();
torch::jit::parseIR(graph0_string, g0.get());
const auto graph1_string = R"IR(
graph(%0 : Float(2, 3, 4),
%1 : Float(2, 3, 4)):
%c1 : Float(2, 3, 4) = aten::mul(%0, %1)
%d1 : Float(2, 3, 4) = aten::mul(%c1, %0)
return (%d1))IR";
auto g1 = std::make_shared<Graph>();
torch::jit::parseIR(graph1_string, g1.get());
auto getFusionGroup = [](const std::shared_ptr<Graph>& graph) {
const auto& nodes = graph->nodes();
auto maybe_fusion_group =
std::find_if(nodes.begin(), nodes.end(), [](const Node* node) {
return node->kind() == prim::FusionGroup;
});
TORCH_CHECK(
maybe_fusion_group != nodes.end(),
"testRegisterFusionCachesKernel: could not create FusionGroup");
return *maybe_fusion_group;
};
// Creates two alpha-equivalent fusion groups
torch::jit::overrideCanFuseOnCPU(true);
FuseGraph(g0);
FuseGraph(g1);
torch::jit::overrideCanFuseOnCPU(false);
auto fg0 = getFusionGroup(g0);
auto fg1 = getFusionGroup(g1);
// Registers both with the fusion compiler.
auto expected_key = registerFusion(fg0);
auto second_key = registerFusion(fg1);
// Because the graphs are alpha-equivalent, they should return the same key
// and therefore share a KernelSpec to share kernels for specializations
ASSERT_EQ(second_key, expected_key);
}
} // namespace jit
} // namespace torch
|
