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
|
#include <torch/csrc/jit/passes/decompose_ops.h>
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/shape_analysis.h>
#include <torch/csrc/jit/passes/utils/subgraph_utils.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <ATen/core/symbol.h>
namespace torch {
namespace jit {
namespace {
c10::AliasAnalysisKind aliasAnalysisFromSchema() {
return c10::AliasAnalysisKind::FROM_SCHEMA;
}
} // namespace
// helper to determine if an optional tensor argument/value passed in is
// statically defined (neither a None constant nor a Optional[Tensor] type)
// return yes, no, or no value if we can't tell
c10::optional<bool> isDefined(Value* tensor) {
if (tensor->type()->isSubtypeOf(*TensorType::get())) {
return true;
}
if (tensor->node()->mustBeNone()) {
return false;
}
return {};
}
bool isDecomposableNorm(Node* normalize_op) {
static const OperatorSet decomposable_normalization_ops = {
"aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor",
"aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, bool cudnn_enable) -> Tensor",
};
Value* input = normalize_op->namedInput(attr::input);
if (!input->type()->isSubtypeOf(*TensorType::get())) {
return false;
}
auto device = input->type()->expectRef<TensorType>().device();
// As of now, we do the decomposition for batchnorm/layernorm on GPU device
// only
if (!device || !(*device).is_cuda()) {
return false;
}
if (normalize_op->isMemberOf(decomposable_normalization_ops)) {
// If we can't determine if weight and bias is defined statically there's
// really no point in decomposing normalization into simpler ops, since it
// won't get fused into a single kernel.
return isDefined(normalize_op->namedInput(attr::weight)).has_value() &&
isDefined(normalize_op->namedInput(attr::bias)).has_value();
}
return false;
}
RegisterOperators reg_ops(
{Operator(
"aten::_ncf_unsqueeze(Tensor(a) self, int ndim) -> Tensor(a)",
[](Stack& stack) {
const int64_t ndim = pop(stack).toInt();
auto self = pop(stack).toTensor();
c10::SmallVector<int64_t, 8> sizes(ndim, 1);
AT_ASSERT(self.dim() == 1);
sizes.at(1) = self.size(0);
push(stack, self.reshape(sizes));
},
aliasAnalysisFromSchema()),
Operator(
"aten::_ncf_view(Tensor(a) self, int[] input_shape, int normalized_ndim) -> Tensor(a)",
[](Stack& stack) {
const int64_t normalized_ndim = pop(stack).toInt();
auto input_shape = pop(stack).toIntList();
auto self = pop(stack).toTensor();
const int64_t input_ndim = input_shape.size();
c10::SmallVector<int64_t, 8> sizes(input_ndim, 1);
for (int i = 0; i < input_ndim - normalized_ndim; ++i) {
sizes.at(i) = input_shape.get(i);
}
push(stack, self.reshape(sizes));
},
aliasAnalysisFromSchema())});
bool DecomposeOps(Block* block, CompilationUnit& decompose_funcs) {
bool decomposed = false;
for (auto it = block->nodes().begin(), end = block->nodes().end(); it != end;
++it) {
for (auto sub : it->blocks()) {
DecomposeOps(sub, decompose_funcs);
}
if (it->matches(
"aten::addmm(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta, Scalar alpha) -> Tensor",
/*const_inputs=*/{attr::beta, attr::alpha})) {
// For the case where we have an addmm where alpha and beta are Attributes
// and both of those scalars are equal to 1.0, decompose this into an mm
// followed by an add so that it can go through the existing optimization
// (batchmm)
if (it->get<at::Scalar>(attr::alpha)->toComplexDouble() != 1.0 ||
it->get<at::Scalar>(attr::beta)->toComplexDouble() != 1.0) {
continue;
}
decomposed = true;
WithInsertPoint guard(*it);
std::shared_ptr<Graph> d_graph =
toGraphFunction(decompose_funcs.get_function("addmm")).graph();
Value* new_output =
insertGraph(*it->owningGraph(), *d_graph, it->inputs()).at(0);
// Set the output of the decomposed graph to have the same output type as
// the original op otherwise the canonicalized graph will have TensorType
// as the output of this node which is incorrect
new_output->setType(it->output()->type());
it->output()->replaceAllUsesWith(new_output);
it.destroyCurrent();
} else if (
it->matches(
"aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensor")) {
if (!isDecomposableNorm(*it)) {
continue;
}
decomposed = true;
WithInsertPoint insert_guard{*it};
Graph* graph = it->owningGraph();
Value* input = it->namedInput(attr::input);
Value* input_dim = graph->insert(aten::dim, {input});
std::vector<Value*> inputs{
input,
it->namedInput(attr::running_mean),
it->namedInput(attr::running_var),
it->namedInput(attr::training),
it->namedInput(attr::momentum),
it->namedInput(attr::eps)};
// inline the compiled decomposed batchnorm
std::shared_ptr<Graph> d_graph =
toGraphFunction(decompose_funcs.get_function("batch_norm")).graph();
Value* new_output = insertGraph(*graph, *d_graph, inputs).at(0);
// post processing the graph
Value* weight = it->namedInput(attr::weight);
Value* bias = it->namedInput(attr::bias);
if (isDefined(weight).value()) {
Value* expanded_weight =
graph->insert(aten::_ncf_unsqueeze, {weight, input_dim});
new_output = graph->insert(aten::mul, {new_output, expanded_weight});
}
if (isDefined(bias).value()) {
Value* expanded_bias =
graph->insert(aten::_ncf_unsqueeze, {bias, input_dim});
new_output = graph->insert(aten::add, {new_output, expanded_bias});
}
it->output()->replaceAllUsesWith(new_output);
it.destroyCurrent();
} else if (
it->matches(
"aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight, Tensor? bias, float eps, bool cudnn_enable) -> Tensor")) {
if (!isDecomposableNorm(*it)) {
continue;
}
decomposed = true;
WithInsertPoint insert_guard{*it};
Graph* graph = it->owningGraph();
std::vector<Value*> inputs{
it->namedInput(attr::input),
it->namedInput(attr::normalized_shape),
it->namedInput(attr::eps),
it->namedInput(attr::cudnn_enable)};
// inline the compiled decomposed layernorm
std::shared_ptr<Graph> d_graph =
toGraphFunction(decompose_funcs.get_function("layer_norm")).graph();
Value* new_output = insertGraph(*graph, *d_graph, inputs).at(0);
// post processing the graph
Value* weight = it->namedInput(attr::weight);
Value* bias = it->namedInput(attr::bias);
if (isDefined(weight).value()) {
new_output = graph->insert(aten::mul, {new_output, weight});
}
if (isDefined(bias).value()) {
new_output = graph->insert(aten::add, {new_output, bias});
}
it->output()->replaceAllUsesWith(new_output);
it.destroyCurrent();
}
}
return decomposed;
}
void DecomposeOps(std::shared_ptr<Graph>& graph) {
static CompilationUnit decompose_funcs(R"SCRIPT(
def addmm(self: Tensor, mat1: Tensor, mat2: Tensor, beta: number = 1.0, alpha: number = 1.0):
return self + mat1.mm(mat2)
def batch_norm(input : Tensor, running_mean : Optional[Tensor], running_var : Optional[Tensor], training : bool, momentum : float, eps : float) -> Tensor:
if training:
norm_mean, norm_var = torch.batch_norm_update_stats(input, running_mean, running_var, momentum)
else:
norm_mean = torch._unwrap_optional(running_mean)
norm_var = torch._unwrap_optional(running_var)
norm_mean = torch._ncf_unsqueeze(norm_mean, input.dim())
norm_var = torch._ncf_unsqueeze(norm_var, input.dim())
norm_invstd = 1 / (torch.sqrt(norm_var + eps))
return ((input - norm_mean) * norm_invstd)
def layer_norm(input : Tensor, normalized_shape : List[int], eps : float, cudnn_enable : bool) -> Tensor:
input_ndim = input.dim()
normalized_ndim = len(normalized_shape)
n = 1
for i in range(input_ndim - normalized_ndim):
n *= input.size(i)
input_reshape = input.contiguous().view(1, n, -1)
mean, invstd = torch.batch_norm_stats(input_reshape, eps)
input_shape = input.size()
mean = torch._ncf_view(mean, input_shape, normalized_ndim)
invstd = torch._ncf_view(invstd, input_shape, normalized_ndim)
return (input - mean) * invstd
)SCRIPT");
bool is_decomposed = DecomposeOps(graph->block(), decompose_funcs);
if (is_decomposed) {
// we only re-run those passes when the graph get decomposed
PropagateInputShapes(graph);
ConstantPropagation(graph);
EliminateDeadCode(graph);
}
}
} // namespace jit
} // namespace torch
|
