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体验新版 GitCode,发现更多精彩内容 >>
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提交
ed0990e7
编写于
2月 07, 2022
作者:
Y
Yan Chunwei
提交者:
GitHub
2月 07, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
INFRT/Refine TensorMap (2nd PR) (#39262)
上级
0c43ce22
变更
17
隐藏空白更改
内联
并排
Showing
17 changed file
with
319 addition
and
13 deletion
+319
-13
paddle/infrt/dialect/basic_kernels.td
paddle/infrt/dialect/basic_kernels.td
+3
-3
paddle/infrt/dialect/dense_tensor.td
paddle/infrt/dialect/dense_tensor.td
+45
-5
paddle/infrt/host_context/kernel_frame.h
paddle/infrt/host_context/kernel_frame.h
+8
-0
paddle/infrt/host_context/kernel_utils.h
paddle/infrt/host_context/kernel_utils.h
+1
-1
paddle/infrt/host_context/value.h
paddle/infrt/host_context/value.h
+16
-0
paddle/infrt/host_context/value_test.cc
paddle/infrt/host_context/value_test.cc
+10
-0
paddle/infrt/kernel/tensor_kernels.cc
paddle/infrt/kernel/tensor_kernels.cc
+63
-4
paddle/infrt/tests/.gitignore
paddle/infrt/tests/.gitignore
+7
-0
paddle/infrt/tests/CMakeLists.txt
paddle/infrt/tests/CMakeLists.txt
+2
-0
paddle/infrt/tests/dialect/tensor/.gitignore
paddle/infrt/tests/dialect/tensor/.gitignore
+5
-0
paddle/infrt/tests/dialect/tensor/dense_tensor.mlir
paddle/infrt/tests/dialect/tensor/dense_tensor.mlir
+24
-0
paddle/infrt/tests/dialect/tensor/naive_kernels.mlir
paddle/infrt/tests/dialect/tensor/naive_kernels.mlir
+35
-0
paddle/infrt/tests/dialect/tensor/tensor_map.mlir.in
paddle/infrt/tests/dialect/tensor/tensor_map.mlir.in
+16
-0
paddle/infrt/tests/dialect/tensor/tensor_shape.mlir
paddle/infrt/tests/dialect/tensor/tensor_shape.mlir
+8
-0
paddle/infrt/tests/dialect/tensor/tensor_type.mlir
paddle/infrt/tests/dialect/tensor/tensor_type.mlir
+10
-0
paddle/scripts/infrt_build.sh
paddle/scripts/infrt_build.sh
+10
-0
tools/infrt/fake_models/multi_fc.py
tools/infrt/fake_models/multi_fc.py
+56
-0
未找到文件。
paddle/infrt/dialect/basic_kernels.td
浏览文件 @
ed0990e7
...
...
@@ -106,10 +106,10 @@ class PrintOp<string suffix, Type type> : INFRT_Op<"print." # suffix> {
let verifier = ?;
}
//
def PrintI32Op : PrintOp<"i32", I32>;
//
def PrintI64Op : PrintOp<"i64", I64>;
def PrintI32Op : PrintOp<"i32", I32>;
def PrintI64Op : PrintOp<"i64", I64>;
def PrintF32Op : PrintOp<"f32", F32>;
//
def PrintF64Op : PrintOp<"f64", F64>;
def PrintF64Op : PrintOp<"f64", F64>;
def GetStringOp : INFRT_Op<"get_string"> {
let summary = "infrt.get_string";
...
...
paddle/infrt/dialect/dense_tensor.td
浏览文件 @
ed0990e7
...
...
@@ -112,23 +112,35 @@ def LoadParamsOp : DT_Op<"load_params", [NoSideEffect]> {
let verifier = ?;
}
def
GetParamOp : DT_Op<"get_param
", [NoSideEffect]> {
let summary = "dt.
get_param
operation";
def
TensorMapGetTensorOp : DT_Op<"tensor_map_get_tensor
", [NoSideEffect]> {
let summary = "dt.
tensor_map_get_tensor
operation";
let description = [{
An operation that can get a tensor from TensorMap.
An operation that can get a tensor from
a
TensorMap.
}];
// input path of model params.
let arguments = (ins
TensorMapType:$map,
Str
Attr
:$name
Str
ingType
:$name
);
let results = (outs TensorType:$output);
let assemblyFormat = "`(`
$map `,` $name
`)` attr-dict `->` type($output)";
let assemblyFormat = "`(`
operands
`)` attr-dict `->` type($output)";
let verifier = ?;
}
def TensorMapGetSizeOp : DT_Op<"tensor_map_get_size", [NoSideEffect]> {
let summary = "ddt.tensor_map_get_size operation";
let description = [{
An operation that get the size of a TensorMap.
}];
let arguments = (ins TensorMapType:$map);
let results = (outs I32:$size);
let assemblyFormat = "`(` $map `)` attr-dict `->` type($size)";
}
def GetTensorShapeOp : DT_Op<"get_tensor_shape", [NoSideEffect]> {
let summary = "dt.get_tensor_shape operation";
...
...
@@ -141,10 +153,38 @@ def GetTensorShapeOp : DT_Op<"get_tensor_shape", [NoSideEffect]> {
let assemblyFormat = "$input attr-dict `:` type($input) `->` type($output)";
}
class NaiveElementwiseAddOp<string dtype> :
DT_Op<"naive_elementwise_add." # dtype, [NoSideEffect]> {
let summary = "dt.naive_elementwise_add operation";
let description = [{
Naive elementwise_add operation.
Just for testing.
}];
let arguments = (ins TensorType:$a, TensorType:$b);
let results = (outs TensorType:$output);
let assemblyFormat = "`(` $a `,` $b `)` attr-dict `:` `(` type($a) `,` type($b) `)` `->` type($output)";
}
class NaiveMatmulOp<string dtype> :
DT_Op<"naive_matmul." # dtype, [NoSideEffect]> {
let summary = "dt.naive_matmul operation";
let description = [{
Naive matmul operation.
Just for testing.
}];
let arguments = (ins TensorType:$x, TensorType:$w);
let results = (outs TensorType:$output);
let assemblyFormat = "`(` $x `,` $w `)` attr-dict `:` `(` type($x) `,` type($w) `)` `->` type($output)";
}
foreach dtype = ["ui8", "ui16", "ui32", "ui64", "i32", "f32", "f64", "i64"] in {
def DT_CreateUninitTensorOp_#dtype : CreateUninitTensorOp<dtype>;
def DT_FillTensorOp_#dtype : FillTensorWithConstantOp<dtype>;
def DT_SetTensorOp_#dtype : SetTensorOp<dtype>;
def DT_NaiveElementwiseAddOp_#dtype : NaiveElementwiseAddOp<dtype>;
def DT_NaiveMatmulOp_#dtype : NaiveMatmulOp<dtype>;
}
#endif // DT_OPS
paddle/infrt/host_context/kernel_frame.h
浏览文件 @
ed0990e7
...
...
@@ -37,6 +37,14 @@ class KernelFrame {
(
num_results_
==
-
1
?
0
:
num_results_
);
}
//! Get something at a specific position \p index. The element might be an
//! argument, an attribute or a result.
template
<
typename
T
>
T
&
GetElementAt
(
int
index
)
{
CHECK_LT
(
index
,
GetNumArgs
()
+
GetNumAttributes
()
+
GetNumResults
());
return
value_or_attrs_
[
index
]
->
template
get_or_default
<
T
>();
}
template
<
typename
T
>
T
&
GetArgAt
(
int
index
)
{
CHECK_LT
(
index
,
GetNumArgs
());
...
...
paddle/infrt/host_context/kernel_utils.h
浏览文件 @
ed0990e7
...
...
@@ -244,7 +244,7 @@ struct KernelImpl<Return (*)(Args...), impl_fn> {
static_assert
(
out_idx
==
0
,
"Arguments should appear before results"
);
static_assert
(
const_idx
==
0
,
"Arguments and results should appear before attributes."
);
auto
*
arg
=
&
frame
->
GetArg
At
<
Head
>
(
in_idx
);
auto
*
arg
=
&
frame
->
template
GetElement
At
<
Head
>(
in_idx
);
KernelCallHelper
<
Tail
...
>::
template
Invoke
<
in_idx
+
1
,
out_idx
,
const_idx
>(
frame
,
pargs
...,
...
...
paddle/infrt/host_context/value.h
浏览文件 @
ed0990e7
...
...
@@ -28,6 +28,9 @@
#include "paddle/infrt/tensor/dense_tensor_view.h"
#include "paddle/infrt/tensor/tensor_map.h"
#include "paddle/infrt/tensor/tensor_shape.h"
// Disabled temporarily for failed compile, will enable latter.
// #include "paddle/pten/backends/cpu/cpu_context.h"
// #include "paddle/pten/core/dense_tensor.h"
namespace
infrt
{
namespace
host_context
{
...
...
@@ -82,13 +85,25 @@ class Value : public common::Object {
template
<
typename
T
>
const
T
&
get
()
const
{
CHECK
(
data
.
template
is
<
T
>());
return
data
.
get
<
T
>
();
}
template
<
typename
T
>
T
&
get
()
{
CHECK
(
data
.
template
is
<
T
>());
return
data
.
get
<
T
>
();
}
//! Get the value if assigned before or return a default value instead.
template
<
class
T
>
T
&
get_or_default
()
{
if
(
!
data
.
template
is
<
T
>())
{
this
->
set
(
T
{});
}
return
get
<
T
>
();
}
template
<
typename
T
>
void
set
(
T
&&
v
)
{
data
=
std
::
move
(
v
);
...
...
@@ -124,6 +139,7 @@ class ValueRef : common::Shared<Value> {
using
common
::
Shared
<
Value
>::
Reset
;
using
common
::
Shared
<
Value
>::
operator
->
;
using
common
::
Shared
<
Value
>::
operator
*
;
//! Get a readonly data.
template
<
typename
T
>
const
T
&
get
()
const
{
...
...
paddle/infrt/host_context/value_test.cc
浏览文件 @
ed0990e7
...
...
@@ -30,5 +30,15 @@ TEST(ValueRef, test) {
ASSERT_EQ
(
z
.
get
<
bool
>
(),
true
);
}
// If the value is not assign, the get_or_default should return a default value.
TEST
(
Value
,
init
)
{
Value
x
;
ASSERT_EQ
(
x
.
get_or_default
<
int
>
(),
0
);
Value
tensor
;
auto
&
t
=
tensor
.
get_or_default
<
tensor
::
DenseHostTensor
>
();
ASSERT_EQ
(
t
.
shape
().
GetRank
(),
0
);
}
}
// namespace host_context
}
// namespace infrt
paddle/infrt/kernel/tensor_kernels.cc
浏览文件 @
ed0990e7
...
...
@@ -53,13 +53,62 @@ TensorMap LoadParams(const std::string &path) {
return
*
(
infrt
::
tensor
::
LoadParams
(
path
));
}
DenseHostTensor
GetParam
(
TensorMap
map
,
Attribute
<
std
::
string
>
nameAttr
)
{
auto
&
name
=
nameAttr
.
get
();
return
*
(
map
[
name
]);
void
TensorMapGetTensor
(
TensorMap
map
,
const
std
::
string
&
name
,
DenseHostTensor
*
out
)
{
auto
it
=
map
.
find
(
name
);
CHECK
(
it
!=
map
.
end
())
<<
"No tensor called "
<<
name
<<
" in the TensorMap"
;
*
out
=
*
it
->
second
;
}
int32_t
TensorMapGetSize
(
TensorMap
map
)
{
return
map
.
size
();
}
DenseHostTensor
ShallowCopyTensor
(
DenseHostTensor
v
)
{
return
v
;
}
template
<
typename
T
>
void
NaiveElementwiseAdd
(
const
DenseHostTensor
&
x
,
const
DenseHostTensor
&
y
,
DenseHostTensor
*
out
)
{
CHECK_EQ
(
x
.
shape
().
GetNumElements
(),
y
.
shape
().
GetNumElements
());
// Infer shape
*
out
=
DenseHostTensor
(
x
.
shape
(),
GetDType
<
T
>
());
const
T
*
x_data
=
static_cast
<
T
*>
(
x
.
raw_data
());
const
T
*
y_data
=
static_cast
<
T
*>
(
y
.
raw_data
());
T
*
out_data
=
static_cast
<
T
*>
(
out
->
raw_data
());
for
(
size_t
i
=
0
,
n
=
x
.
shape
().
GetNumElements
();
i
<
n
;
i
++
)
{
out_data
[
i
]
=
x_data
[
i
]
+
y_data
[
i
];
}
}
//! A naive implementation for x matmul w
template
<
typename
T
>
void
NaiveMatmul
(
const
DenseHostTensor
&
x
,
const
DenseHostTensor
&
w
,
DenseHostTensor
*
out
)
{
CHECK_EQ
(
x
.
shape
().
GetRank
(),
2
);
CHECK_EQ
(
w
.
shape
().
GetRank
(),
2
);
CHECK_EQ
(
x
.
shape
().
GetDim
(
x
.
shape
().
GetRank
()
-
1
),
w
.
shape
().
GetDim
(
0
));
std
::
vector
<
int64_t
>
out_dims
({
x
.
shape
().
GetDim
(
0
),
w
.
shape
().
GetDim
(
1
)});
*
out
=
DenseHostTensor
(
TensorShape
(
out_dims
),
GetDType
<
T
>
());
auto
*
out_data
=
static_cast
<
T
*>
(
out
->
raw_data
());
auto
*
x_data
=
static_cast
<
const
T
*>
(
x
.
raw_data
());
auto
*
w_data
=
static_cast
<
const
T
*>
(
w
.
raw_data
());
const
int
M
=
x
.
shape
().
GetDim
(
0
);
const
int
K
=
x
.
shape
().
GetDim
(
1
);
const
int
N
=
w
.
shape
().
GetDim
(
1
);
for
(
int
i
=
0
;
i
<
M
;
i
++
)
{
for
(
int
j
=
0
;
j
<
N
;
j
++
)
{
for
(
int
k
=
0
;
k
<
K
;
k
++
)
{
out_data
[
i
*
N
+
j
]
+=
x_data
[
i
*
K
+
k
]
*
w_data
[
k
*
N
+
j
];
}
}
}
}
/// ===== Kernel end ====
void
RegisterTensorKernels
(
host_context
::
KernelRegistry
*
registry
)
{
...
...
@@ -71,10 +120,20 @@ void RegisterTensorKernels(host_context::KernelRegistry *registry) {
INFRT_KERNEL
(
FillTensorWithConstant
<
float
>
));
registry
->
AddKernel
(
"dt.fill_tensor_with_constant.f64"
,
INFRT_KERNEL
(
FillTensorWithConstant
<
double
>
));
// TensorMap related methods.
registry
->
AddKernel
(
"dt.load_params"
,
INFRT_KERNEL
(
LoadParams
));
registry
->
AddKernel
(
"dt.get_param"
,
INFRT_KERNEL
(
GetParam
));
registry
->
AddKernel
(
"dt.tensor_map_get_tensor"
,
INFRT_KERNEL
(
TensorMapGetTensor
));
registry
->
AddKernel
(
"dt.tensor_map_get_size"
,
INFRT_KERNEL
(
TensorMapGetSize
));
registry
->
AddKernel
(
"dt.shallow_copy_tensor"
,
INFRT_KERNEL
(
ShallowCopyTensor
));
// Naive kernels.
registry
->
AddKernel
(
"dt.naive_elementwise_add.f32"
,
INFRT_KERNEL
(
NaiveElementwiseAdd
<
float
>
));
registry
->
AddKernel
(
"dt.naive_matmul.f32"
,
INFRT_KERNEL
(
NaiveMatmul
<
float
>
));
}
}
// namespace kernel
...
...
paddle/infrt/tests/.gitignore
0 → 100644
浏览文件 @
ed0990e7
.DS_Store
.idea
*.log
tmp/
Output
paddle/infrt/tests/CMakeLists.txt
浏览文件 @
ed0990e7
...
...
@@ -2,3 +2,5 @@ configure_file(lit.cfg.py.in "${CMAKE_SOURCE_DIR}/paddle/infrt/tests/lit.cfg.py"
add_test
(
NAME test_infrt_by_lit COMMAND sh -c
"lit -v
${
CMAKE_SOURCE_DIR
}
/paddle/infrt/tests --filter-out
\"
disabled_*
\"
"
DEPENDS infrtopt infrtexec
)
configure_file
(
${
CMAKE_CURRENT_SOURCE_DIR
}
/dialect/tensor/tensor_map.mlir.in
${
CMAKE_CURRENT_SOURCE_DIR
}
/dialect/tensor/tensor_map.mlir
)
paddle/infrt/tests/dialect/tensor/.gitignore
0 → 100644
浏览文件 @
ed0990e7
.DS_Store
.idea
*.log
tmp/
tensor_map.mlir
paddle/infrt/tests/dialect/tensor/dense_tensor.mlir
0 → 100644
浏览文件 @
ed0990e7
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: dense_shape0
func @dense_shape0() {
%shape = ts.build_shape [1:i64, 57:i64]
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.tensor<X86, NCHW, F32>
infrt.return
}
func @predict(%a: !infrt.tensor<X86, NCHW, F32>, %b: !infrt.tensor<X86, NCHW, F32>) -> (!infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>) {
%a0 = dt.shallow_copy_tensor %a : !infrt.tensor<X86, NCHW, F32> -> !infrt.tensor<X86, NCHW, F32>
%b0 = dt.shallow_copy_tensor %b : !infrt.tensor<X86, NCHW, F32> -> !infrt.tensor<X86, NCHW, F32>
infrt.return %a0, %b0: !infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>
}
func @main() {
%shape = ts.build_shape [1:i64, 57:i64]
%a = dt.create_uninit_tensor.f32 [12:i64, 23:i64] -> !infrt.tensor<X86, NCHW, F32>
%b, %c = infrt.call @predict(%a, %a) : (!infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>) -> (!infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/naive_kernels.mlir
0 → 100644
浏览文件 @
ed0990e7
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: naive_elementwise_add
func @naive_elementwise_add() {
// create a
%a = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.tensor<X86, NCHW, F32>
dt.fill_tensor_with_constant.f32 (%a : !infrt.tensor<X86, NCHW, F32>) {value=1.0:f32}
// create b
%b = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.tensor<X86, NCHW, F32>
dt.fill_tensor_with_constant.f32 (%b : !infrt.tensor<X86, NCHW, F32>) {value=2.0:f32}
// get c
%c = dt.naive_elementwise_add.f32(%a, %b) {} : (!infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>) -> !infrt.tensor<X86, NCHW, F32>
// CHECK: tensor: shape=shape[2,8], values=[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
dt.print_tensor (%c : !infrt.tensor<X86, NCHW, F32>)
infrt.return
}
// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: naive_matmul
func @naive_matmul() {
// create a
%a = dt.create_uninit_tensor.f32 [2:i64, 8:i64] -> !infrt.tensor<X86, NCHW, F32>
dt.fill_tensor_with_constant.f32 (%a : !infrt.tensor<X86, NCHW, F32>) {value=1.0:f32}
// create b
%b = dt.create_uninit_tensor.f32 [8:i64, 4:i64] -> !infrt.tensor<X86, NCHW, F32>
dt.fill_tensor_with_constant.f32 (%b : !infrt.tensor<X86, NCHW, F32>) {value=2.0:f32}
// get c
%c = dt.naive_matmul.f32(%a, %b) {} : (!infrt.tensor<X86, NCHW, F32>, !infrt.tensor<X86, NCHW, F32>) -> !infrt.tensor<X86, NCHW, F32>
// CHECK: tensor: shape=shape[2,4], values=[16, 16, 16, 16, 16, 16, 16, 16]
dt.print_tensor (%c : !infrt.tensor<X86, NCHW, F32>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_map.mlir.in
0 → 100644
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ed0990e7
// RUN: infrtexec -i %s | FileCheck %s
func @load_tensor_map() {
%path = infrt.get_string("@CMAKE_BINARY_DIR@/multi_fc_model")
%map = dt.load_params(%path)
%size = dt.tensor_map_get_size(%map) -> i32
infrt.print.i32 %size
%tensor_name = infrt.get_string("fc_bias")
%a = dt.tensor_map_get_tensor(%map, %tensor_name) -> !infrt.tensor<X86, NCHW, F32>
// CHECK: tensor: shape=shape[2], values=[0, 0]
dt.print_tensor (%a : !infrt.tensor<X86, NCHW, F32>)
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_shape.mlir
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// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: @build_tensor1
func @build_tensor1() {
%a = ts.build_shape [1:i64, 57:i64, 92:i64]
// CHECK: shape[1,57,92]
ts.print_shape %a
infrt.return
}
paddle/infrt/tests/dialect/tensor/tensor_type.mlir
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// RUN: infrtexec -i %s | FileCheck %s
// CHECK-LABEL: test_tensor_type
func @test_tensor_type() {
%a = dt.create_uninit_tensor.f32 [3, 4] -> !infrt.tensor<X86, NCHW, F32>
dt.fill_tensor_with_constant.f32 (%a : !infrt.tensor<X86, NCHW, F32>) {value=1.0:f32}
// CHECK: tensor: shape=shape[3,4], values=[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
dt.print_tensor (%a : !infrt.tensor<X86, NCHW, F32>)
infrt.return
}
paddle/scripts/infrt_build.sh
浏览文件 @
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...
...
@@ -100,7 +100,17 @@ function infrt_gen_and_build() {
echo
"ipipe_log_param_Infrt_Build_Time:
$[
$endTime_s
-
$startTime_s
]s"
>>
${
PADDLE_ROOT
}
/build/infrt_summary.txt
}
function
create_fake_models
()
{
cd
${
PADDLE_ROOT
}
/build
# create multi_fc model, this will generate "multi_fc_model"
python3
-m
pip uninstall
-y
paddlepaddle
python3
-m
pip
install
paddlepaddle
python3
${
PADDLE_ROOT
}
/tools/infrt/fake_models/multi_fc.py
}
function
test_infrt
()
{
create_fake_models
# install llvm-lit toolkit
python3
-m
pip
install
lit
...
...
tools/infrt/fake_models/multi_fc.py
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浏览文件 @
ed0990e7
# Copyright (c) 2021 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.
"""
A fake model with multiple FC layers to test CINN on a more complex model.
"""
import
numpy
import
sys
,
os
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.backward
import
append_backward
size
=
2
num_layers
=
4
paddle
.
enable_static
()
a
=
fluid
.
layers
.
data
(
name
=
"A"
,
shape
=
[
-
1
,
size
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
"label"
,
shape
=
[
size
],
dtype
=
'float32'
)
fc_out
=
fluid
.
layers
.
fc
(
input
=
a
,
size
=
size
,
act
=
"relu"
,
bias_attr
=
fluid
.
ParamAttr
(
name
=
"fc_bias"
),
num_flatten_dims
=
1
)
for
i
in
range
(
num_layers
-
1
):
fc_out
=
fluid
.
layers
.
fc
(
input
=
fc_out
,
size
=
size
,
act
=
"relu"
,
bias_attr
=
fluid
.
ParamAttr
(
name
=
"fc_bias"
),
num_flatten_dims
=
1
)
cost
=
fluid
.
layers
.
square_error_cost
(
fc_out
,
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
optimizer
.
minimize
(
avg_cost
)
cpu
=
fluid
.
core
.
CPUPlace
()
loss
=
exe
=
fluid
.
Executor
(
cpu
)
exe
.
run
(
fluid
.
default_startup_program
())
fluid
.
io
.
save_inference_model
(
"./multi_fc_model"
,
[
a
.
name
],
[
fc_out
],
exe
)
print
(
'output name'
,
fc_out
.
name
)
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