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体验新版 GitCode,发现更多精彩内容 >>
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7987a905
编写于
10月 10, 2022
作者:
Z
zhoutianzi666
提交者:
GitHub
10月 10, 2022
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差异文件
[Paddle-TRT] support new quant format from slim (#46022)
上级
6e4cba14
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
153 addition
and
14 deletion
+153
-14
paddle/fluid/framework/ir/delete_quant_dequant_linear_op_pass.cc
...fluid/framework/ir/delete_quant_dequant_linear_op_pass.cc
+4
-6
paddle/fluid/inference/api/paddle_pass_builder.cc
paddle/fluid/inference/api/paddle_pass_builder.cc
+2
-2
paddle/fluid/inference/tensorrt/convert/matmul_v2_op.cc
paddle/fluid/inference/tensorrt/convert/matmul_v2_op.cc
+24
-6
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+1
-0
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py
...ests/unittests/ir/inference/test_trt_convert_matmul_v2.py
+122
-0
未找到文件。
paddle/fluid/framework/ir/delete_quant_dequant_linear_op_pass.cc
浏览文件 @
7987a905
...
@@ -111,9 +111,6 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
...
@@ -111,9 +111,6 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
}
}
*/
*/
std
::
unordered_set
<
const
Node
*>
nodes2rm
=
{};
std
::
unordered_set
<
const
Node
*>
nodes2rm
=
{};
int
bit_length
=
PADDLE_GET_CONST
(
int
,
quantize_linear_op
->
Op
()
->
GetAttr
(
"bit_length"
));
int
range
=
((
1
<<
(
bit_length
-
1
))
-
1
);
// Get input scale from tensor
// Get input scale from tensor
const
LoDTensor
&
input_scale_tensor
=
const
LoDTensor
&
input_scale_tensor
=
...
@@ -124,7 +121,7 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
...
@@ -124,7 +121,7 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
platform
::
errors
::
InvalidArgument
(
platform
::
errors
::
InvalidArgument
(
"Input scale tensor's place should be CPU."
));
"Input scale tensor's place should be CPU."
));
const
float
*
input_scale_data
=
input_scale_tensor
.
data
<
float
>
();
const
float
*
input_scale_data
=
input_scale_tensor
.
data
<
float
>
();
float
input_scale
=
input_scale_data
[
0
]
/
range
;
float
input_scale
=
input_scale_data
[
0
];
int
nums_any_ops
=
dequantize_linear_op_out
->
outputs
.
size
();
int
nums_any_ops
=
dequantize_linear_op_out
->
outputs
.
size
();
for
(
int
i
=
0
;
i
<
nums_any_ops
;
++
i
)
{
for
(
int
i
=
0
;
i
<
nums_any_ops
;
++
i
)
{
...
@@ -138,8 +135,9 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
...
@@ -138,8 +135,9 @@ void DeleteQuantDequantLinearOpPass::ApplyImpl(ir::Graph* graph) const {
IR_NODE_LINK_TO
(
quantize_linear_op_x
,
IR_NODE_LINK_TO
(
quantize_linear_op_x
,
dequantize_linear_op_out
->
outputs
[
i
]);
dequantize_linear_op_out
->
outputs
[
i
]);
}
}
// Forbid removing weight tensor when weight is shared between ops
nodes2rm
.
insert
(
quantize_linear_op_scale
);
if
(
quantize_linear_op_scale
->
outputs
.
size
()
<=
1UL
)
nodes2rm
.
insert
(
quantize_linear_op_scale
);
nodes2rm
.
insert
(
quantize_linear_op
);
nodes2rm
.
insert
(
quantize_linear_op
);
nodes2rm
.
insert
(
quantize_linear_op_out
);
nodes2rm
.
insert
(
quantize_linear_op_out
);
nodes2rm
.
insert
(
dequantize_linear_op
);
nodes2rm
.
insert
(
dequantize_linear_op
);
...
...
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @
7987a905
...
@@ -84,8 +84,7 @@ void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
...
@@ -84,8 +84,7 @@ void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
void
PaddlePassBuilder
::
ClearPasses
()
{
passes_
.
clear
();
}
void
PaddlePassBuilder
::
ClearPasses
()
{
passes_
.
clear
();
}
const
std
::
vector
<
std
::
string
>
kTRTSubgraphPasses
({
const
std
::
vector
<
std
::
string
>
kTRTSubgraphPasses
({
"identity_scale_op_clean_pass"
,
//
"adaptive_pool2d_convert_global_pass"
,
//
"adaptive_pool2d_convert_global_pass"
,
//
"shuffle_channel_detect_pass"
,
//
"shuffle_channel_detect_pass"
,
//
"quant_conv2d_dequant_fuse_pass"
,
//
"quant_conv2d_dequant_fuse_pass"
,
//
"delete_fill_constant_op_pass"
,
//
"delete_fill_constant_op_pass"
,
//
...
@@ -93,6 +92,7 @@ const std::vector<std::string> kTRTSubgraphPasses({
...
@@ -93,6 +92,7 @@ const std::vector<std::string> kTRTSubgraphPasses({
"delete_quant_dequant_filter_op_pass"
,
//
"delete_quant_dequant_filter_op_pass"
,
//
"delete_weight_dequant_linear_op_pass"
,
//
"delete_weight_dequant_linear_op_pass"
,
//
"delete_quant_dequant_linear_op_pass"
,
//
"delete_quant_dequant_linear_op_pass"
,
//
"identity_scale_op_clean_pass"
,
//
"add_support_int8_pass"
,
//
"add_support_int8_pass"
,
//
// "fc_fuse_pass", //
// "fc_fuse_pass", //
"simplify_with_basic_ops_pass"
,
//
"simplify_with_basic_ops_pass"
,
//
...
...
paddle/fluid/inference/tensorrt/convert/matmul_v2_op.cc
浏览文件 @
7987a905
...
@@ -37,9 +37,9 @@ class MatMulV2OpConverter : public OpConverter {
...
@@ -37,9 +37,9 @@ class MatMulV2OpConverter : public OpConverter {
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
void
operator
()(
const
framework
::
proto
::
OpDesc
&
op
,
const
framework
::
Scope
&
scope
,
const
framework
::
Scope
&
scope
,
bool
test_mode
)
override
{
bool
test_mode
)
override
{
VLOG
(
3
)
<<
"convert a
fluid matmul_v2 op to tensorrt matmul
layer "
;
VLOG
(
3
)
<<
"convert a
matmul_v2 op to tensorrt IMatrixMultiplyLayer
layer "
;
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
framework
::
OpDesc
op_desc
(
op
,
nullptr
);
nvinfer1
::
ILayer
*
layer
=
nullptr
;
nvinfer1
::
I
MatrixMultiply
Layer
*
layer
=
nullptr
;
// Declare inputs
// Declare inputs
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
auto
*
input1
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"X"
)[
0
]);
...
@@ -61,8 +61,9 @@ class MatMulV2OpConverter : public OpConverter {
...
@@ -61,8 +61,9 @@ class MatMulV2OpConverter : public OpConverter {
:
nvinfer1
::
MatrixOperation
::
kNONE
;
:
nvinfer1
::
MatrixOperation
::
kNONE
;
int
one_num
=
0
;
int
one_num
=
0
;
bool
all_matrix
=
dims_x
.
nbDims
>=
2
&&
dims_y
.
nbDims
>=
2
;
nvinfer1
::
ITensor
*
new_shape_tensor
=
nullptr
;
nvinfer1
::
ITensor
*
new_shape_tensor
=
nullptr
;
if
(
dims_x
.
nbDims
<
dims_y
.
nbDims
)
{
if
(
dims_x
.
nbDims
<
dims_y
.
nbDims
&&
all_matrix
)
{
one_num
=
dims_y
.
nbDims
-
dims_x
.
nbDims
;
one_num
=
dims_y
.
nbDims
-
dims_x
.
nbDims
;
new_shape_tensor
=
Shape
(
input1
);
new_shape_tensor
=
Shape
(
input1
);
std
::
vector
<
int32_t
>
one_vec
(
one_num
,
1
);
std
::
vector
<
int32_t
>
one_vec
(
one_num
,
1
);
...
@@ -80,7 +81,7 @@ class MatMulV2OpConverter : public OpConverter {
...
@@ -80,7 +81,7 @@ class MatMulV2OpConverter : public OpConverter {
*
input2
,
*
input2
,
matrix_operation_Y
);
matrix_operation_Y
);
}
else
if
(
dims_x
.
nbDims
>
dims_y
.
nbDims
)
{
}
else
if
(
dims_x
.
nbDims
>
dims_y
.
nbDims
&&
all_matrix
)
{
one_num
=
dims_x
.
nbDims
-
dims_y
.
nbDims
;
one_num
=
dims_x
.
nbDims
-
dims_y
.
nbDims
;
new_shape_tensor
=
Shape
(
input2
);
new_shape_tensor
=
Shape
(
input2
);
std
::
vector
<
int32_t
>
one_vec
(
one_num
,
1
);
std
::
vector
<
int32_t
>
one_vec
(
one_num
,
1
);
...
@@ -105,9 +106,26 @@ class MatMulV2OpConverter : public OpConverter {
...
@@ -105,9 +106,26 @@ class MatMulV2OpConverter : public OpConverter {
*
input2
,
*
input2
,
matrix_operation_Y
);
matrix_operation_Y
);
}
}
VLOG
(
3
)
<<
"Convert a fluid matmul_v2_op_float to TensorRT "
;
if
(
dims_x
.
nbDims
==
1
)
layer
->
setOperation
(
0
,
nvinfer1
::
MatrixOperation
::
kVECTOR
);
if
(
dims_y
.
nbDims
==
1
)
layer
->
setOperation
(
1
,
nvinfer1
::
MatrixOperation
::
kVECTOR
);
nvinfer1
::
ILayer
*
final_layer
=
static_cast
<
nvinfer1
::
ILayer
*>
(
layer
);
// When vec * vec, trt produces a scalar, so to be consistent with paddle,
// we need add a reshape.
if
(
dims_x
.
nbDims
==
1
&&
dims_y
.
nbDims
==
1
)
{
auto
reshape_layer
=
TRT_ENGINE_ADD_LAYER
(
engine_
,
Shuffle
,
*
layer
->
getOutput
(
0
));
nvinfer1
::
Dims
reshape_dim
;
reshape_dim
.
nbDims
=
1
;
reshape_dim
.
d
[
0
]
=
1
;
reshape_layer
->
setReshapeDimensions
(
reshape_dim
);
final_layer
=
static_cast
<
nvinfer1
::
ILayer
*>
(
reshape_layer
);
}
VLOG
(
3
)
<<
"Convert a matmul_v2_op to TensorRT "
;
RreplenishLayerAndOutput
(
layer
,
"matmul_v2_op"
,
{
output_name
},
test_mode
);
RreplenishLayerAndOutput
(
final_layer
,
"matmul_v2_op"
,
{
output_name
},
test_mode
);
}
}
};
};
...
...
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
7987a905
...
@@ -46,6 +46,7 @@ struct SimpleOpTypeSetTeller : public Teller {
...
@@ -46,6 +46,7 @@ struct SimpleOpTypeSetTeller : public Teller {
#if IS_TRT_VERSION_GE(7000)
#if IS_TRT_VERSION_GE(7000)
teller_set
.
insert
(
"tile"
);
teller_set
.
insert
(
"tile"
);
teller_set
.
insert
(
"flatten_contiguous_range"
);
teller_set
.
insert
(
"flatten_contiguous_range"
);
int8_teller_set
.
insert
(
"flatten_contiguous_range"
);
teller_set
.
insert
(
"rnn"
);
teller_set
.
insert
(
"rnn"
);
int8_teller_set
.
insert
(
"rnn"
);
int8_teller_set
.
insert
(
"rnn"
);
teller_set
.
insert
(
"fill_constant_batch_size_like"
);
teller_set
.
insert
(
"fill_constant_batch_size_like"
);
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py
浏览文件 @
7987a905
...
@@ -193,5 +193,127 @@ class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest):
...
@@ -193,5 +193,127 @@ class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest):
self
.
run_test
()
self
.
run_test
()
class
TrtConvertMatmulTest_dynamic3
(
TrtLayerAutoScanTest
):
def
sample_program_configs
(
self
):
def
generate_input
(
shape
):
return
np
.
random
.
random
(
shape
).
astype
(
np
.
float32
)
# case0: mat * vec
# case1: vec * mat
# case2: vec * vec
for
case
in
[
0
,
1
,
2
]:
for
batch
in
range
(
20
,
23
):
for
trans_x
in
[
False
,
True
]:
for
trans_y
in
[
False
,
True
]:
self
.
case
=
case
input1_shape
=
[]
input2_shape
=
[]
if
case
==
0
:
input1_shape
=
[
batch
,
50
]
input2_shape
=
[
50
]
elif
case
==
1
:
input1_shape
=
[
50
]
input2_shape
=
[
50
,
batch
]
elif
case
==
2
:
input1_shape
=
[
50
]
input2_shape
=
[
50
]
if
(
case
==
0
or
case
==
1
):
dics
=
[{
"trans_x"
:
False
,
"trans_y"
:
False
,
}]
elif
(
case
==
2
):
dics
=
[{
"trans_x"
:
trans_x
,
"trans_y"
:
trans_y
,
}]
ops_config
=
[{
"op_type"
:
"matmul_v2"
,
"op_inputs"
:
{
"X"
:
[
"input1_data"
],
"Y"
:
[
"input2_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input1_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
input1_shape
)),
"input2_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input
,
input2_shape
))
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
():
if
(
self
.
case
==
0
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input1_data"
:
[
20
,
50
],
"input2_data"
:
[
50
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input1_data"
:
[
30
,
50
],
"input2_data"
:
[
50
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input1_data"
:
[
25
,
50
],
"input2_data"
:
[
50
]
}
elif
(
self
.
case
==
1
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input2_data"
:
[
50
,
20
],
"input1_data"
:
[
50
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input2_data"
:
[
50
,
30
],
"input1_data"
:
[
50
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input2_data"
:
[
50
,
25
],
"input1_data"
:
[
50
]
}
elif
(
self
.
case
==
2
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input2_data"
:
[
30
],
"input1_data"
:
[
50
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input2_data"
:
[
50
],
"input1_data"
:
[
50
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input2_data"
:
[
50
],
"input1_data"
:
[
50
]
}
generate_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
def
add_skip_trt_case
(
self
):
pass
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
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