-
Notifications
You must be signed in to change notification settings - Fork 5.5k
/
transpose_op.cc
391 lines (350 loc) · 15.6 KB
/
transpose_op.cc
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
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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 "paddle/fluid/operators/transpose_op.h"
#include <memory>
#include <string>
#include <vector>
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
using framework::Tensor;
class TransposeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Transpose");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Transpose");
auto x_dims = ctx->GetInputDim("X");
std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
size_t x_rank = x_dims.size();
size_t axis_size = axis.size();
PADDLE_ENFORCE_EQ(x_rank, axis_size,
platform::errors::InvalidArgument(
"The input tensor's dimension "
"should be equal to the axis's size. "
"But received input tensor's dimension is %d, "
"axis's size is %d",
x_rank, axis_size));
std::vector<int> count(axis_size, 0);
for (size_t i = 0; i < axis_size; i++) {
PADDLE_ENFORCE_GE(axis[i], 0,
platform::errors::InvalidArgument(
"The axis should be greater than or equal to 0."
"But received %d of axis[%d]",
axis[i], i));
PADDLE_ENFORCE_EQ(
axis[i] < static_cast<int>(axis_size) && ++count[axis[i]] == 1, true,
platform::errors::InvalidArgument(
"Each element of Attribute axis should "
"be a unique value range from 0 to (dims - 1), "
"where the dims is the axis's size, "
"unique value means this axis value can appear only once. "
"But received axis[%d] is %d, axis_size is %d, "
"count[axis[%d]] is %d",
i, axis[i], axis_size, i, count[axis[i]]));
}
framework::DDim out_dims(x_dims);
#ifdef PADDLE_WITH_MKLDNN
// Here we need to match dims to paddle layout
// as we are producing non-oneDNN result
if ((x_dims.size() >= 3) &&
(paddle::platform::MKLDNNDeviceContext::tls()
.get_cur_paddle_data_layout() == framework::DataLayout::kNHWC)) {
auto dims = framework::vectorize<int>(x_dims);
std::rotate(dims.begin() + 1, dims.begin() + 2, dims.end());
x_dims = x_dims.reshape(dims);
VLOG(3)
<< "Rotating Shape in Transpose from: kMKLDNN to: kNHWC output_shape";
}
#endif
for (size_t i = 0; i < axis_size; i++) {
out_dims[i] = x_dims[axis[i]];
}
ctx->SetOutputDim("Out", out_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
this->CanMKLDNNBeUsed(ctx, data_type)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(data_type, ctx.GetPlace(), layout_,
library_);
}
};
class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"X",
"(Tensor) The input tensor, tensors with rank up to 6 are supported.");
AddOutput("Out", "(Tensor)The output tensor.");
AddAttr<std::vector<int>>(
"axis",
"(vector<int>) A list of values, and the size of the list should be "
"the same with the input tensor rank. This operator permutes the input "
"tensor's axes according to the values given.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false)
.AsExtra();
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout")
.AsExtra();
AddAttr<bool>(
"use_quantizer",
"(bool, default false) "
"This parameter is no longer used. Use 'mkldnn_data_type' instead.")
.SetDefault(false);
AddAttr<std::string>(
"mkldnn_data_type",
"(string, default \"float32\"). Data type of mkldnn kernel")
.SetDefault("float32")
.InEnum({"float32", "int8", "bfloat16"});
/* int8 parameters */
AddComment(R"DOC(
Transpose Operator.
The input tensor will be permuted according to the axes given.
The behavior of this operator is similar to how `numpy.transpose` works.
- suppose the input `X` is a 2-D tensor:
$$
X = \begin{pmatrix}
0 &1 &2 \\
3 &4 &5
\end{pmatrix}$$
the given `axes` is: $[1, 0]$, and $Y$ = transpose($X$, axis)
then the output $Y$ is:
$$
Y = \begin{pmatrix}
0 &3 \\
1 &4 \\
2 &5
\end{pmatrix}$$
- Given a input tensor with shape $(N, C, H, W)$ and the `axes` is
$[0, 2, 3, 1]$, then shape of the output tensor will be: $(N, H, W, C)$.
)DOC");
}
};
class TransposeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "TransposeOpGrad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "TransposeOpGrad");
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
auto data_type = OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out"));
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
this->CanMKLDNNBeUsed(ctx, data_type)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(data_type, ctx.GetPlace(), layout_,
library_);
}
};
// FIXME(zcd): transpose2 adds an intermediate output(XShape) based on
// transpose, the XShape is used to carry the shape and lod of X which
// will be used in transpose_grad, in this way, the framework can reuse
// the memory of X immediately the transpose2_op is finished.
// Considering compatibility issues, we could not fix transpose2_op
class Transpose2Op : public TransposeOp {
public:
Transpose2Op(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: TransposeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
TransposeOp::InferShape(ctx);
OP_INOUT_CHECK(ctx->HasOutput("XShape"), "Output", "XShape", "Transpose2");
const auto &in_dims = ctx->GetInputDim("X");
std::vector<int64_t> x_shape_dim(in_dims.size() + 1);
x_shape_dim[0] = 0;
for (int i = 0; i < in_dims.size(); ++i) {
x_shape_dim[i + 1] = in_dims[i];
}
ctx->SetOutputDim("XShape", framework::make_ddim(x_shape_dim));
ctx->ShareLoD("X", /*->*/ "XShape");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
int customized_type_value =
framework::OpKernelType::kDefaultCustomizedTypeValue;
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::proto::VarType::Type data_type =
OperatorWithKernel::IndicateVarDataType(ctx, "X");
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
this->CanMKLDNNBeUsed(ctx, data_type)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
using framework::proto::VarType;
auto input_data_type = ctx.Input<Tensor>("X")->type();
customized_type_value = (input_data_type == VarType::INT8 ||
input_data_type == VarType::UINT8)
? kTransposeMKLDNNINT8
: kTransposeMKLDNNFP32;
}
#endif
return framework::OpKernelType(data_type, ctx.GetPlace(), layout_, library_,
customized_type_value);
}
};
class Transpose2OpMaker : public TransposeOpMaker {
public:
void Make() override {
TransposeOpMaker::Make();
AddOutput("XShape", "(Tensor)The output tensor.")
.AsIntermediate()
.AsExtra();
}
};
template <typename T>
class Transpose2GradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("transpose2_grad");
grad_op->SetInput("XShape", this->Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
grad_op->SetAttrMap(this->Attrs());
}
};
template <typename T>
class Transpose2DoubleGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("transpose2");
grad_op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
grad_op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
grad_op->SetOutput("XShape", this->Input("XShape"));
grad_op->SetAttrMap(this->Attrs());
}
};
class Transpose2OpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("XShape"), "Input", "XShape",
"Transpose2OpGrad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "Transpose2OpGrad");
if (ctx->HasOutput(framework::GradVarName("X"))) {
auto xshape_dim = ctx->GetInputDim("XShape");
auto x_shape_dim =
framework::slice_ddim(xshape_dim, 1, xshape_dim.size());
ctx->SetOutputDim(framework::GradVarName("X"), x_shape_dim);
ctx->ShareLoD("XShape", framework::GradVarName("X"));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::proto::VarType::Type data_type =
OperatorWithKernel::IndicateVarDataType(ctx,
framework::GradVarName("Out"));
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
this->CanMKLDNNBeUsed(ctx, data_type)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(data_type, ctx.GetPlace(), layout_,
library_);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(
transpose, ops::TransposeOp, ops::TransposeOpMaker,
paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>);
REGISTER_OPERATOR(transpose_grad, ops::TransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
transpose, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CPU_KERNEL(
transpose_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OPERATOR(transpose2, ops::Transpose2Op, ops::Transpose2OpMaker,
ops::Transpose2GradMaker<paddle::framework::OpDesc>,
ops::Transpose2GradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(transpose2_grad, ops::Transpose2OpGrad,
ops::Transpose2DoubleGradMaker<paddle::framework::OpDesc>,
ops::Transpose2DoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(
transpose2, ops::TransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::TransposeKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CPU_KERNEL(
transpose2_grad,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, int32_t>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::TransposeGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);