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
|
#include <torch/csrc/jit/backends/backend.h>
#include <torch/csrc/jit/backends/backend_debug_handler.h>
#include <torch/csrc/jit/backends/backend_preprocess.h>
namespace torch {
namespace jit {
// This test JIT backend is intended to do the minimal amount of work
// necessary to test that the JIT backend registration endpoints and
// code generation are working correctly. It is not intended to
// produce numerically correct results.
template <bool isAvailable>
class TestBackend : public PyTorchBackendInterface {
public:
// Constructor.
// NOLINTNEXTLINE(modernize-use-equals-default)
explicit TestBackend() {}
// NOLINTNEXTLINE(modernize-use-override)
virtual ~TestBackend() = default;
bool is_available() override {
return isAvailable;
}
c10::impl::GenericDict compile(
c10::IValue processed,
c10::impl::GenericDict method_compile_spec) override {
auto spec =
c10::impl::toTypedDict<std::string, at::IValue>(method_compile_spec);
// Return the same string as a value for every key in method_compile_spec.
auto handles = c10::Dict<std::string, std::string>();
for (const auto& it : spec) {
handles.insert(it.key(), it.key());
}
return c10::impl::toGenericDict(handles);
}
c10::impl::GenericList execute(
c10::IValue handle,
c10::impl::GenericList inputs) override {
TORCH_INTERNAL_ASSERT(handle.isString());
TORCH_INTERNAL_ASSERT(inputs.size() > 0);
c10::List<at::Tensor> output_list;
// Implement simple accumulator and negative accumulator (?) ops. Return one
// or both of them depending on the handle to make sure multiple outputs are
// handled.
c10::IValue value = inputs[0];
at::Tensor accum = value.toTensor();
accum = accum.clone();
at::Tensor sub_accum = value.toTensor();
sub_accum = sub_accum.clone();
for (size_t i = 1, e = inputs.size(); i < e; ++i) {
value = inputs[i];
accum.add_(value.toTensor(), 1.0);
sub_accum.sub_(value.toTensor(), 1.0);
}
if (handle.toStringRef() == "accum") {
output_list.emplace_back(accum);
} else if (handle.toStringRef() == "sub_accum") {
output_list.emplace_back(sub_accum);
} else if (handle.toStringRef() == "forward") {
output_list.emplace_back(accum);
output_list.emplace_back(sub_accum);
}
return c10::impl::toList(output_list);
}
};
namespace {
c10::IValue preprocess(
const Module& mod,
const c10::Dict<IValue, IValue>& method_compile_spec,
const BackendDebugHandleGenerator& generate_debug_handles) {
return mod._ivalue();
}
constexpr auto backend_name = "test_backend";
static auto cls_available =
torch::jit::backend<TestBackend<true>>(backend_name);
static auto pre_reg = backend_preprocess_register(backend_name, preprocess);
constexpr auto backend_unavailable_name = "test_backend_unavailable";
static auto cls_unavailable =
torch::jit::backend<TestBackend<false>>(backend_unavailable_name);
static auto pre_reg_unavailable =
backend_preprocess_register(backend_unavailable_name, preprocess);
} // namespace
} // namespace jit
} // namespace torch
|
