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体验新版 GitCode,发现更多精彩内容 >>
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31efe00a
编写于
9月 06, 2022
作者:
W
Wangzheee
提交者:
GitHub
9月 06, 2022
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电子邮件补丁
差异文件
[Paddle-Inference] remove int8 fallback (#45762)
* remove int8 fallback
上级
efccf896
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
5 addition
and
71 deletion
+5
-71
paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
...fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
+5
-0
paddle/fluid/inference/tensorrt/engine.cc
paddle/fluid/inference/tensorrt/engine.cc
+0
-71
未找到文件。
paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
浏览文件 @
31efe00a
...
...
@@ -72,11 +72,15 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
}
auto
*
shape_tensor
=
Shape
(
mask_id_tensor
);
std
::
vector
<
nvinfer1
::
ITensor
*>
start_vec_tensor
;
std
::
vector
<
nvinfer1
::
ITensor
*>
size_vec_tensor
;
for
(
int
i
=
0
;
i
<
mask_dims
.
nbDims
;
i
++
)
{
start_vec_tensor
.
push_back
(
Add1DConstantLayer
(
0
));
size_vec_tensor
.
push_back
(
Add1DConstantLayer
(
1
));
}
size_vec_tensor
[
1
]
=
GetEleTensorOfShape
(
shape_tensor
,
1
);
auto
start_tensor
=
Concat
(
start_vec_tensor
);
auto
size_tensor
=
Concat
(
size_vec_tensor
);
auto
slice_layer
=
...
...
@@ -86,6 +90,7 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
slice_start_dims
,
slice_start_dims
,
slice_stride_dims
);
// unuseful slice_start_dims
slice_layer
->
setInput
(
1
,
*
start_tensor
);
slice_layer
->
setInput
(
2
,
*
size_tensor
);
slice_layer
->
setName
(
(
"Embeltwise_slice_layer (Output: slice_max_seqlen "
+
...
...
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @
31efe00a
...
...
@@ -89,9 +89,7 @@ void TensorRTEngine::Execute(int batch_size,
if
(
!
with_dynamic_shape
())
{
infer_context
->
enqueue
(
batch_size
,
buffers
->
data
(),
stream
,
nullptr
);
}
else
{
#if IS_TRT_VERSION_GE(6000)
infer_context
->
enqueueV2
(
buffers
->
data
(),
stream
,
nullptr
);
#endif
}
SetRuntimeBatch
(
batch_size
);
}
...
...
@@ -134,7 +132,6 @@ void TensorRTEngine::FreezeNetwork() {
}
else
{
infer_builder_config_
->
setInt8Calibrator
(
nullptr
);
#if IS_TRT_VERSION_GE(5000)
for
(
auto
&
quant_range
:
quant_dynamic_range_
)
{
auto
tensor
=
quant_range
.
first
;
float
range
=
quant_range
.
second
;
...
...
@@ -160,72 +157,6 @@ void TensorRTEngine::FreezeNetwork() {
<<
", this might be ok when trt does not need this range"
;
}
}
#if IS_TRT_VERSION_GE(5122)
auto
layer_int8_fallback
=
[
&
](
nvinfer1
::
ILayer
*
layer
)
->
bool
{
if
(
layer
->
getType
()
==
nvinfer1
::
LayerType
::
kSHAPE
)
{
return
false
;
}
bool
all_int
=
true
;
for
(
int
j
=
0
;
j
<
layer
->
getNbInputs
();
j
++
)
{
auto
*
temp_in
=
layer
->
getInput
(
j
);
if
(
temp_in
->
getType
()
!=
nvinfer1
::
DataType
::
kINT32
)
{
all_int
=
false
;
}
}
for
(
int
j
=
0
;
j
<
layer
->
getNbOutputs
();
j
++
)
{
auto
*
temp_out
=
layer
->
getOutput
(
j
);
if
(
temp_out
->
getType
()
!=
nvinfer1
::
DataType
::
kINT32
)
{
all_int
=
false
;
}
}
if
(
all_int
)
return
false
;
for
(
int
j
=
0
;
j
<
layer
->
getNbInputs
();
j
++
)
{
auto
*
temp_in
=
layer
->
getInput
(
j
);
if
(
!
temp_in
->
dynamicRangeIsSet
())
{
VLOG
(
1
)
<<
"Layer(Name: "
<<
layer
->
getName
()
<<
") is set to float32 because its input("
<<
temp_in
->
getName
()
<<
") doesn't have dynamic range."
;
return
true
;
}
}
for
(
int
j
=
0
;
j
<
layer
->
getNbOutputs
();
j
++
)
{
auto
*
temp_out
=
layer
->
getOutput
(
j
);
if
(
!
temp_out
->
dynamicRangeIsSet
())
{
VLOG
(
1
)
<<
"Layer(Name: "
<<
layer
->
getName
()
<<
") is set to float32 because its output("
<<
temp_out
->
getName
()
<<
") doesn't have dynamic range."
;
return
true
;
}
}
return
false
;
};
// If a layer's output is the network's output, or not all of its inputs
// and outputs have scales,
// this layer's precision and output type are set to float32.
// This step has no effect if this layer is fused during TRT optimization.
int
layers_no_int8
=
0
;
for
(
int
i
=
0
;
i
<
network
()
->
getNbLayers
();
i
++
)
{
auto
layer
=
network
()
->
getLayer
(
i
);
if
(
layer_int8_fallback
(
layer
))
{
layer
->
setPrecision
(
nvinfer1
::
DataType
::
kFLOAT
);
++
layers_no_int8
;
}
}
// Disable int8 or build engine failed if all layers aren't int8
if
(
layers_no_int8
==
network
()
->
getNbLayers
())
{
nvinfer1
::
BuilderFlags
flags
=
infer_builder_config_
->
getFlags
();
flags
=
flags
&
~
(
1U
<<
static_cast
<
int
>
(
nvinfer1
::
BuilderFlag
::
kINT8
));
// reset flags
infer_builder_config_
->
setFlags
(
flags
);
}
#else
LOG
(
WARNING
)
<<
"If your TensorRT version is lower than 5.1.2.2, you "
"must provide quantization scales for all tensors using "
"TRT to run."
;
#endif
#endif
}
}
...
...
@@ -265,7 +196,6 @@ void TensorRTEngine::FreezeNetwork() {
}
if
(
with_dynamic_shape_
)
{
#if IS_TRT_VERSION_GE(6000)
LOG
(
INFO
)
<<
"Run Paddle-TRT Dynamic Shape mode."
;
for
(
int
i
=
0
;
i
<
max_profile_num_
;
i
++
)
{
for
(
auto
&
input
:
min_input_shape_
)
{
...
...
@@ -310,7 +240,6 @@ void TensorRTEngine::FreezeNetwork() {
"'config.SetDynamicShapeInfo(min_shape, max_shape, "
"opt_shape, false /*disable_trt_plugin_fp16*/)'"
;
}
#endif
}
#if IS_TRT_VERSION_GE(8200)
if
(
use_inspector_
)
{
...
...
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