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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
d0a921ba
编写于
7月 06, 2020
作者:
W
Wojciech Uss
提交者:
GitHub
7月 06, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Quant2 updates and fixes (#25313)
上级
869d59cc
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
228 addition
and
149 deletion
+228
-149
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
+11
-5
python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
...luid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
+39
-20
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
+18
-18
python/paddle/fluid/contrib/slim/tests/quant2_int8_image_classification_comparison.py
...slim/tests/quant2_int8_image_classification_comparison.py
+88
-54
python/paddle/fluid/contrib/slim/tests/quant2_int8_nlp_comparison.py
...le/fluid/contrib/slim/tests/quant2_int8_nlp_comparison.py
+65
-32
python/paddle/fluid/contrib/slim/tests/save_quant_model.py
python/paddle/fluid/contrib/slim/tests/save_quant_model.py
+7
-20
未找到文件。
paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
浏览文件 @
d0a921ba
...
...
@@ -46,10 +46,8 @@ void LogCannotQuantizeOp(Node* op, const char* details = nullptr) {
}
void
LogScaleIsMissingForVar
(
Node
*
var
)
{
std
::
stringstream
msg_ss
;
msg_ss
<<
"Quantization scale for the variable "
<<
var
->
Name
()
<<
" is missing."
;
PrettyLogDetail
(
msg_ss
.
str
().
c_str
());
VLOG
(
4
)
<<
"Quantization scale for the variable "
<<
var
->
Name
()
<<
" is missing."
;
}
void
LogQuantizationDisabled
(
Node
*
op
)
{
...
...
@@ -256,6 +254,14 @@ void CPUQuantizePass::QuantizeConv(Graph* graph,
GET_IR_NODE_FROM_SUBGRAPH
(
conv_input
,
conv_input
,
conv_pattern
);
GET_IR_NODE_FROM_SUBGRAPH
(
conv_output
,
conv_output
,
conv_pattern
);
auto
has_output_scale
=
AreScalesPresentForNodes
(
conv_op
,
{
conv_output
});
if
(
with_residual_data
&&
!
has_output_scale
)
{
LogCannotQuantizeOp
(
conv_op
,
"Conv op with ResidualData input cannot be quantized "
"without output scale."
);
return
;
}
if
(
with_residual_data
)
{
GET_IR_NODE_FROM_SUBGRAPH
(
conv_residual_data
,
conv_residual_data
,
conv_pattern
);
...
...
@@ -294,7 +300,7 @@ void CPUQuantizePass::QuantizeConv(Graph* graph,
conv_op
->
Op
()
->
SetAttr
(
"Scale_weights"
,
filter_scale
);
// if quantization scale is missing for output tensor, return fp32 data
if
(
AreScalesPresentForNodes
(
conv_op
,
{
conv_output
})
)
{
if
(
has_output_scale
)
{
bool
is_output_unsigned
{
false
};
auto
output_scale
=
GetScaleValueForNode
(
conv_output
,
&
is_output_unsigned
);
...
...
python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
浏览文件 @
d0a921ba
...
...
@@ -55,7 +55,7 @@ class Quant2Int8MkldnnPass(object):
'fake_dequantize_max_abs'
,
'fake_channel_wise_dequantize_max_abs'
]
self
.
_ops_to_quantize
=
_ops_to_quantize
self
.
_op_ids_to_skip
=
_op_ids_to_skip
if
_op_ids_to_skip
!=
None
else
set
(
self
.
_op_ids_to_skip
=
_op_ids_to_skip
if
_op_ids_to_skip
is
not
None
else
set
(
[
-
1
])
self
.
_scale_immutable_ops
=
[
'transpose2'
,
'reshape2'
,
'pool2d'
,
'scale'
...
...
@@ -71,11 +71,14 @@ class Quant2Int8MkldnnPass(object):
self
.
_var_quant_scales
=
{}
self
.
_max_range
=
{}
self
.
_s8_max
=
127
self
.
_pass_idx
=
0
self
.
_pass_group
=
'int8'
def
apply
(
self
,
graph
):
assert
isinstance
(
graph
,
IrGraph
),
'graph must be the instance of IrGraph.'
self
.
_reset_pass_idx_and_group
(
'int8'
)
graph
=
self
.
_gather_weight_scales_from_fake
(
graph
)
graph
=
self
.
_gather_output_scales_from_attr
(
graph
)
graph
=
self
.
_gather_input_scales_from_fake
(
graph
)
...
...
@@ -86,21 +89,24 @@ class Quant2Int8MkldnnPass(object):
graph
=
self
.
_update_relu_output_scales
(
graph
)
graph
=
self
.
_propagate_scales
(
graph
)
graph
=
self
.
_quantize_fp32_graph
(
graph
)
graph
=
self
.
_
optimize_int8_graph
(
graph
)
graph
=
self
.
_
final_optimizations
(
graph
)
graph
=
self
.
_cleanup
(
graph
)
return
graph
def
apply
_fp32
(
self
,
graph
):
def
prepare_and_optimize
_fp32
(
self
,
graph
):
assert
isinstance
(
graph
,
IrGraph
),
'graph must be the instance of IrGraph.'
graph
=
self
.
_gather_weight_scales_from_fake
(
graph
)
graph
=
self
.
_remove_fake_ops
(
graph
)
graph
=
self
.
_dequantize_weights
(
graph
)
self
.
_reset_pass_idx_and_group
(
'fp32'
)
graph
=
self
.
_optimize_fp32_graph
(
graph
)
graph
=
self
.
_final_optimizations
(
graph
)
graph
=
self
.
_cleanup
(
graph
)
return
graph
def
_reset_pass_idx_and_group
(
self
,
group
):
self
.
_pass_idx
=
0
self
.
_pass_group
=
group
def
_convert_scale2tensor
(
self
,
scale
):
tensor
=
core
.
LoDTensor
()
tensor
.
set
(
scale
,
core
.
CPUPlace
())
...
...
@@ -333,20 +339,38 @@ class Quant2Int8MkldnnPass(object):
def
_optimize_fp32_graph
(
self
,
graph
):
graph
=
self
.
_update_activations
(
graph
)
graph
=
self
.
_remove_ctrl_vars
(
graph
)
graph
=
self
.
_apply_pass
(
graph
,
'attention_lstm_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'seqconv_eltadd_relu_fuse_pass'
)
# graph = self._apply_pass(graph, 'seqpool_concat_fuse_pass')
graph
=
self
.
_apply_pass
(
graph
,
'seqpool_cvm_concat_fuse_pass'
)
# graph = self._apply_pass(graph, 'embedding_fc_lstm_fuse_pass')
graph
=
self
.
_apply_pass
(
graph
,
'fc_lstm_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'mul_lstm_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'fc_gru_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'mul_gru_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'seq_concat_fc_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'squared_mat_sub_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'is_test_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'mkldnn_placement_pass'
,
[
'mkldnn_enabled_op_types'
],
[
set
()])
graph
=
self
.
_apply_pass
(
graph
,
'depthwise_conv_mkldnn_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_bn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_eltwiseadd_bn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_transpose_bn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_transpose_eltwiseadd_bn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_bias_mkldnn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_elementwise_add_mkldnn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_relu_mkldnn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'conv_relu6_mkldnn_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'fc_fuse_pass'
,
[
'use_gpu'
,
'use_fc_padding'
],
[
False
,
False
])
graph
=
self
.
_apply_pass
(
graph
,
'repeated_fc_relu_fuse_pass'
)
if
self
.
_is_fc_quantized
(
graph
):
graph
=
self
.
_apply_pass
(
graph
,
'fc_mkldnn_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'matmul_transpose_reshape_fuse_pass'
)
# the following pass should be the last one since it will work on all fused ops.
graph
=
self
.
_apply_pass
(
graph
,
'runtime_context_cache_pass'
)
return
graph
def
_apply_pass
(
self
,
graph
,
pass_name
,
attrs
=
None
,
attr_values
=
None
):
...
...
@@ -362,12 +386,13 @@ class Quant2Int8MkldnnPass(object):
ir_pass
.
set
(
attr
,
value
)
ir_pass
.
apply
(
cpp_graph
)
if
self
.
_debug
:
graph
.
draw
(
'.'
,
'
quant_fp32_{}'
.
format
(
pass_name
)
,
graph
.
all_op_nodes
())
graph
.
draw
(
'.'
,
'
{}_{}_{}'
.
format
(
self
.
_pass_group
,
self
.
_pass_idx
,
pass_name
),
graph
.
all_op_nodes
())
self
.
_remove_unused_var_nodes
(
graph
)
self
.
_pass_idx
+=
1
return
graph
def
_
optimize_int8_graph
(
self
,
graph
):
def
_
final_optimizations
(
self
,
graph
):
# remove dropout ops
graph
=
self
.
_apply_pass
(
graph
,
'simplify_with_basic_ops_pass'
)
# make some MKL-DNN ops working inplace
...
...
@@ -448,8 +473,7 @@ class Quant2Int8MkldnnPass(object):
self
.
_var_quant_scales
[
out_name
]
=
(
True
,
tensor
)
return
graph
conv_predicate
=
lambda
op
:
op
.
attr
(
"fuse_activation"
)
in
self
.
_relu_ops
and
\
op
.
attr
(
"fuse_residual_connection"
)
==
False
conv_predicate
=
lambda
op
:
op
.
attr
(
"fuse_activation"
)
in
self
.
_relu_ops
graph
=
_set_unsigned_scale
(
graph
,
self
.
_conv_ops
,
"Output"
,
conv_predicate
)
...
...
@@ -465,15 +489,10 @@ class Quant2Int8MkldnnPass(object):
return
'NHWC'
if
self
.
_is_conv_quantized
(
graph
)
else
'NCHW'
def
_quantize_fp32_graph
(
self
,
graph
):
ir_pass
=
self
.
_core
.
get_pass
(
'cpu_quantize_placement_pass'
)
cpp_graph
=
graph
.
graph
ir_pass
.
set
(
'quantize_enabled_op_types'
,
self
.
_ops_to_quantize
)
ir_pass
.
set
(
'quantize_excluded_op_ids'
,
self
.
_find_avg_pooling_ids
(
graph
))
ir_pass
.
apply
(
cpp_graph
)
if
self
.
_debug
:
graph
.
draw
(
'.'
,
'quant_int8_{}'
.
format
(
ir_pass
.
type
()),
graph
.
all_op_nodes
())
graph
=
self
.
_apply_pass
(
graph
,
'cpu_quantize_placement_pass'
,
[
'quantize_enabled_op_types'
,
'quantize_excluded_op_ids'
],
[
self
.
_ops_to_quantize
,
self
.
_find_avg_pooling_ids
(
graph
)])
graph
=
self
.
_apply_pass
(
graph
,
'scale_matmul_fuse_pass'
)
graph
=
self
.
_apply_pass
(
graph
,
'reshape_transpose_matmul_mkldnn_fuse_pass'
)
...
...
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
浏览文件 @
d0a921ba
...
...
@@ -57,7 +57,7 @@ endfunction()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25
function
(
inference_quant2_int8_image_classification_test target quant_model_dir fp32_model_dir dataset_path
ops_to_quantize
)
function
(
inference_quant2_int8_image_classification_test target quant_model_dir fp32_model_dir dataset_path
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/quant2_int8_image_classification_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
...
...
@@ -67,12 +67,11 @@ function(inference_quant2_int8_image_classification_test target quant_model_dir
--infer_data
${
dataset_path
}
--batch_size 10
--batch_num 2
--acc_diff_threshold 0.1
--ops_to_quantize
${
ops_to_quantize
}
)
--acc_diff_threshold 0.1
)
endfunction
()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 20
function
(
inference_quant2_int8_nlp_test target quant_model_dir fp32_model_dir dataset_path labels_path
)
function
(
inference_quant2_int8_nlp_test target quant_model_dir fp32_model_dir dataset_path labels_path
ops_to_quantize
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/quant2_int8_nlp_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
...
...
@@ -83,7 +82,8 @@ function(inference_quant2_int8_nlp_test target quant_model_dir fp32_model_dir da
--labels
${
labels_path
}
--batch_size 10
--batch_num 2
--acc_diff_threshold 0.1
)
--acc_diff_threshold 0.1
--ops_to_quantize
${
ops_to_quantize
}
)
endfunction
()
function
(
download_quant_data install_dir data_file
)
...
...
@@ -98,20 +98,20 @@ function(download_quant_model install_dir data_file)
endif
()
endfunction
()
function
(
save_quant_ic_model_test target quant_model_dir fp32_model_save_path int8_model_save_path
ops_to_quantize
)
function
(
save_quant_ic_model_test target quant_model_dir fp32_model_save_path int8_model_save_path
)
py_test
(
${
target
}
SRCS
${
CMAKE_CURRENT_SOURCE_DIR
}
/save_quant_model.py
ARGS --quant_model_path
${
quant_model_dir
}
--fp32_model_save_path
${
fp32_model_save_path
}
--int8_model_save_path
${
int8_model_save_path
}
--ops_to_quantize
${
ops_to_quantize
}
--debug
)
endfunction
()
function
(
save_quant_nlp_model_test target quant_model_dir fp32_model_save_path int8_model_save_path
)
function
(
save_quant_nlp_model_test target quant_model_dir fp32_model_save_path int8_model_save_path
ops_to_quantize
)
py_test
(
${
target
}
SRCS
${
CMAKE_CURRENT_SOURCE_DIR
}
/save_quant_model.py
ARGS --quant_model_path
${
quant_model_dir
}
--fp32_model_save_path
${
fp32_model_save_path
}
--int8_model_save_path
${
int8_model_save_path
}
)
--int8_model_save_path
${
int8_model_save_path
}
--ops_to_quantize
${
ops_to_quantize
}
)
endfunction
()
function
(
convert_model2dot_test target model_path save_graph_dir save_graph_name
)
...
...
@@ -224,36 +224,34 @@ if(LINUX AND WITH_MKLDNN)
### Quant2 for image classification
set
(
QUANT2_IC_OPS_TO_QUANTIZE
"conv2d,pool2d"
)
# Quant2 ResNet50 with input/output scales in `fake_quantize_moving_average_abs_max` operators,
# with weight scales in `fake_dequantize_max_abs` operators
set
(
QUANT2_RESNET50_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2"
)
set
(
QUANT2_RESNET50_MODEL_ARCHIVE
"ResNet50_qat_perf.tar.gz"
)
download_quant_model
(
${
QUANT2_RESNET50_MODEL_DIR
}
${
QUANT2_RESNET50_MODEL_ARCHIVE
}
)
set
(
FP32_RESNET50_MODEL_DIR
"
${
INT8_INSTALL_DIR
}
/resnet50"
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_mkldnn
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QUANT2_IC_OPS_TO_QUANTIZE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_mkldnn
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes,
# with weight scales in `fake_dequantize_max_abs` operators
set
(
QUANT2_RESNET50_RANGE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_range"
)
set
(
QUANT2_RESNET50_RANGE_MODEL_ARCHIVE
"ResNet50_qat_range.tar.gz"
)
download_quant_model
(
${
QUANT2_RESNET50_RANGE_MODEL_DIR
}
${
QUANT2_RESNET50_RANGE_MODEL_ARCHIVE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_range_mkldnn
${
QUANT2_RESNET50_RANGE_MODEL_DIR
}
/ResNet50_qat_range
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QUANT2_IC_OPS_TO_QUANTIZE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_range_mkldnn
${
QUANT2_RESNET50_RANGE_MODEL_DIR
}
/ResNet50_qat_range
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# Quant2 ResNet50 with input/output scales in `fake_quantize_range_abs_max` operators and the `out_threshold` attributes,
# with weight scales in `fake_channel_wise_dequantize_max_abs` operators
set
(
QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_channelwise"
)
set
(
QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
"ResNet50_qat_channelwise.tar.gz"
)
download_quant_model
(
${
QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
}
${
QUANT2_RESNET50_CHANNELWISE_MODEL_ARCHIVE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_channelwise_mkldnn
${
QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
}
/ResNet50_qat_channelwise
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QUANT2_IC_OPS_TO_QUANTIZE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_resnet50_channelwise_mkldnn
${
QUANT2_RESNET50_CHANNELWISE_MODEL_DIR
}
/ResNet50_qat_channelwise
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# Quant2 MobileNetV1
set
(
QUANT2_MOBILENETV1_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/MobileNetV1_quant2"
)
set
(
QUANT2_MOBILENETV1_MODEL_ARCHIVE
"MobileNet_qat_perf.tar.gz"
)
download_quant_model
(
${
QUANT2_MOBILENETV1_MODEL_DIR
}
${
QUANT2_MOBILENETV1_MODEL_ARCHIVE
}
)
set
(
FP32_MOBILENETV1_MODEL_DIR
"
${
INT8_INSTALL_DIR
}
/mobilenetv1"
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_mobilenetv1_mkldnn
${
QUANT2_MOBILENETV1_MODEL_DIR
}
/MobileNet_qat_perf/float
${
FP32_MOBILENETV1_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QUANT2_IC_OPS_TO_QUANTIZE
}
)
inference_quant2_int8_image_classification_test
(
test_quant2_int8_mobilenetv1_mkldnn
${
QUANT2_MOBILENETV1_MODEL_DIR
}
/MobileNet_qat_perf/float
${
FP32_MOBILENETV1_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
### Quant2 for NLP
...
...
@@ -263,6 +261,8 @@ if(LINUX AND WITH_MKLDNN)
set
(
NLP_LABLES_PATH
"
${
NLP_DATA_DIR
}
/Ernie_dataset/label.xnli.dev"
)
download_quant_data
(
${
NLP_DATA_DIR
}
${
NLP_DATA_ARCHIVE
}
)
set
(
QUANT2_NLP_OPS_TO_QUANTIZE
"fc,reshape2,transpose2,matmul,elementwise_add"
)
# Quant2 Ernie
set
(
QUANT2_ERNIE_MODEL_ARCHIVE
"ernie_qat.tar.gz"
)
set
(
QUANT2_ERNIE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2"
)
...
...
@@ -270,17 +270,17 @@ if(LINUX AND WITH_MKLDNN)
set
(
FP32_ERNIE_MODEL_ARCHIVE
"ernie_fp32_model.tar.gz"
)
set
(
FP32_ERNIE_MODEL_DIR
"
${
QUANT_INSTALL_DIR
}
/Ernie_float"
)
download_quant_fp32_model
(
${
FP32_ERNIE_MODEL_DIR
}
${
FP32_ERNIE_MODEL_ARCHIVE
}
)
inference_quant2_int8_nlp_test
(
test_quant2_int8_ernie_mkldnn
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
FP32_ERNIE_MODEL_DIR
}
/ernie_fp32_model
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
)
inference_quant2_int8_nlp_test
(
test_quant2_int8_ernie_mkldnn
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
FP32_ERNIE_MODEL_DIR
}
/ernie_fp32_model
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
${
QUANT2_NLP_OPS_TO_QUANTIZE
}
)
### Save FP32 model or INT8 model from Quant model
set
(
QUANT2_INT8_RESNET50_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_int8"
)
set
(
QUANT2_FP32_RESNET50_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/ResNet50_quant2_fp32"
)
save_quant_ic_model_test
(
save_quant2_model_resnet50
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
QUANT2_FP32_RESNET50_SAVE_PATH
}
${
QUANT2_INT8_RESNET50_SAVE_PATH
}
${
QUANT2_IC_OPS_TO_QUANTIZE
}
)
save_quant_ic_model_test
(
save_quant2_model_resnet50
${
QUANT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf/float
${
QUANT2_FP32_RESNET50_SAVE_PATH
}
${
QUANT2_INT8_RESNET50_SAVE_PATH
}
)
set
(
QUANT2_INT8_ERNIE_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_int8"
)
set
(
QUANT2_FP32_ERNIE_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_fp32"
)
save_quant_nlp_model_test
(
save_quant2_model_ernie
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
QUANT2_FP32_ERNIE_SAVE_PATH
}
${
QUANT2_INT8_ERNIE_SAVE_PATH
}
)
save_quant_nlp_model_test
(
save_quant2_model_ernie
${
QUANT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
QUANT2_FP32_ERNIE_SAVE_PATH
}
${
QUANT2_INT8_ERNIE_SAVE_PATH
}
${
QUANT2_NLP_OPS_TO_QUANTIZE
}
)
# Convert Quant2 model to dot and pdf files
set
(
QUANT2_INT8_ERNIE_DOT_SAVE_PATH
"
${
QUANT_INSTALL_DIR
}
/Ernie_quant2_int8_dot_file"
)
...
...
python/paddle/fluid/contrib/slim/tests/quant2_int8_image_classification_comparison.py
浏览文件 @
d0a921ba
...
...
@@ -167,7 +167,8 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
batch_size
=
1
,
batch_num
=
1
,
skip_batch_num
=
0
,
transform_to_int8
=
False
):
target
=
'quant'
):
assert
target
in
[
'quant'
,
'int8'
,
'fp32'
]
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
executor
.
global_scope
()
...
...
@@ -183,17 +184,19 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
graph
=
IrGraph
(
core
.
Graph
(
inference_program
.
desc
),
for_test
=
True
)
if
(
self
.
_debug
):
graph
.
draw
(
'.'
,
'quant_orig'
,
graph
.
all_op_nodes
())
if
(
transform_to_int8
):
transform_to_mkldnn_int8_pass
=
Quant2Int8MkldnnPass
(
self
.
_quantized_ops
,
_op_ids_to_skip
=
self
.
_op_ids_to_skip
,
_scope
=
inference_scope
,
_place
=
place
,
_core
=
core
,
_debug
=
self
.
_debug
)
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
else
:
quant_transform_pass
=
Quant2Int8MkldnnPass
(
self
.
_quantized_ops
,
_op_ids_to_skip
=
self
.
_op_ids_to_skip
,
_scope
=
inference_scope
,
_place
=
place
,
_core
=
core
,
_debug
=
self
.
_debug
)
if
(
target
==
'quant'
):
graph
=
self
.
_prepare_for_fp32_mkldnn
(
graph
)
elif
(
target
==
'int8'
):
graph
=
quant_transform_pass
.
apply
(
graph
)
else
:
# target == fp32
graph
=
quant_transform_pass
.
prepare_and_optimize_fp32
(
graph
)
inference_program
=
graph
.
to_program
()
...
...
@@ -222,18 +225,7 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
images
=
np
.
array
(
images
).
astype
(
'float32'
)
labels
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
)
if
(
transform_to_int8
==
True
):
# INT8 models obtained from Quant models do not have accuracy measuring layers
start
=
time
.
time
()
out
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
images
},
fetch_list
=
fetch_targets
)
batch_time
=
(
time
.
time
()
-
start
)
*
1000
# in miliseconds
outputs
.
append
(
out
[
0
])
# Calculate accuracy result
batch_acc1
,
batch_acc5
=
self
.
_get_batch_accuracy
(
out
[
0
],
labels
)
else
:
if
(
target
==
'fp32'
):
# FP32 models have accuracy measuring layers
labels
=
labels
.
reshape
([
-
1
,
1
])
start
=
time
.
time
()
...
...
@@ -246,6 +238,18 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
batch_time
=
(
time
.
time
()
-
start
)
*
1000
# in miliseconds
batch_acc1
,
batch_acc5
=
out
[
1
][
0
],
out
[
2
][
0
]
outputs
.
append
(
batch_acc1
)
else
:
# Quant INT8 models do not have accuracy measuring layers
start
=
time
.
time
()
out
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
images
},
fetch_list
=
fetch_targets
)
batch_time
=
(
time
.
time
()
-
start
)
*
1000
# in miliseconds
outputs
.
append
(
out
[
0
])
# Calculate accuracy result
batch_acc1
,
batch_acc5
=
self
.
_get_batch_accuracy
(
out
[
0
],
labels
)
infer_accs1
.
append
(
batch_acc1
)
infer_accs5
.
append
(
batch_acc5
)
samples
=
len
(
data
)
...
...
@@ -274,28 +278,37 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
return
outputs
,
acc1_avg
,
acc5_avg
,
fps_avg
,
latency_avg
def
_summarize_performance
(
self
,
fp32_fps
,
fp32_lat
,
int8_fps
,
int8_lat
):
def
_print_performance
(
self
,
title
,
fps
,
lat
):
_logger
.
info
(
'{0}: avg fps: {1:.2f}, avg latency: {2:.4f} ms'
.
format
(
title
,
fps
,
lat
))
def
_print_accuracy
(
self
,
title
,
acc1
,
acc5
):
_logger
.
info
(
'{0}: avg top1 accuracy: {1:.4f}, avg top5 accuracy: {2:.4f}'
.
format
(
title
,
acc1
,
acc5
))
def
_summarize_performance
(
self
,
int8_fps
,
int8_lat
,
fp32_fps
,
fp32_lat
):
_logger
.
info
(
'--- Performance summary ---'
)
_logger
.
info
(
'FP32: avg fps: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
fp32_fps
,
fp32_lat
))
_logger
.
info
(
'INT8: avg fps: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
int8_fps
,
int8_lat
))
self
.
_print_performance
(
'INT8'
,
int8_fps
,
int8_lat
)
if
fp32_lat
>=
0
:
self
.
_print_performance
(
'FP32'
,
fp32_fps
,
fp32_lat
)
def
_
compare_accuracy
(
self
,
fp32_acc1
,
fp32
_acc5
,
int8_acc1
,
int8_acc5
,
threshold
):
def
_
summarize_accuracy
(
self
,
quant_acc1
,
quant
_acc5
,
int8_acc1
,
int8_acc5
,
fp32_acc1
,
fp32_acc5
):
_logger
.
info
(
'--- Accuracy summary ---'
)
self
.
_print_accuracy
(
'Quant'
,
quant_acc1
,
quant_acc5
)
self
.
_print_accuracy
(
'INT8'
,
int8_acc1
,
int8_acc5
)
if
fp32_acc1
>=
0
:
self
.
_print_accuracy
(
'FP32'
,
fp32_acc1
,
fp32_acc5
)
def
_compare_accuracy
(
self
,
threshold
,
quant_acc1
,
int8_acc1
):
_logger
.
info
(
'Accepted top1 accuracy drop threshold: {0}. (condition: (
FP32_top1_acc - IN8_top1_acc) <= threshold
)'
'Accepted top1 accuracy drop threshold: {0}. (condition: (
Quant_top1_acc - IN8_top1_acc) <= threshold && Quant_top1_acc > 0.5 && INT8_top1_acc > 0.5
)'
.
format
(
threshold
))
_logger
.
info
(
'FP32: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'
.
format
(
fp32_acc1
,
fp32_acc5
))
_logger
.
info
(
'INT8: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'
.
format
(
int8_acc1
,
int8_acc5
))
assert
fp32_acc1
>
0.0
assert
int8_acc1
>
0.0
assert
fp32_acc1
-
int8_acc1
<=
threshold
# We assume valid accuracy to be at least 0.5
assert
quant_acc1
>
0.5
assert
int8_acc1
>
0.5
assert
quant_acc1
-
int8_acc1
<=
threshold
def
test_graph_transformation
(
self
):
if
not
fluid
.
core
.
is_compiled_with_mkldnn
():
...
...
@@ -303,10 +316,9 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
quant_model_path
=
test_case_args
.
quant_model
assert
quant_model_path
,
'The Quant model path cannot be empty. Please, use the --quant_model option.'
fp32_model_path
=
test_case_args
.
fp32_model
assert
fp32_model_path
,
'The FP32 model path cannot be empty. Please, use the --fp32_model option.'
data_path
=
test_case_args
.
infer_data
assert
data_path
,
'The dataset path cannot be empty. Please, use the --infer_data option.'
fp32_model_path
=
test_case_args
.
fp32_model
batch_size
=
test_case_args
.
batch_size
batch_num
=
test_case_args
.
batch_num
skip_batch_num
=
test_case_args
.
skip_batch_num
...
...
@@ -323,9 +335,10 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
self
.
_op_ids_to_skip
=
set
(
map
(
int
,
test_case_args
.
op_ids_to_skip
.
split
(
','
)))
_logger
.
info
(
'
FP32 & Quant
INT8 prediction run.'
)
_logger
.
info
(
'
Quant &
INT8 prediction run.'
)
_logger
.
info
(
'Quant model: {}'
.
format
(
quant_model_path
))
_logger
.
info
(
'FP32 model: {}'
.
format
(
fp32_model_path
))
if
fp32_model_path
:
_logger
.
info
(
'FP32 model: {}'
.
format
(
fp32_model_path
))
_logger
.
info
(
'Dataset: {}'
.
format
(
data_path
))
_logger
.
info
(
'Batch size: {}'
.
format
(
batch_size
))
_logger
.
info
(
'Batch number: {}'
.
format
(
batch_num
))
...
...
@@ -336,17 +349,20 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
map
(
str
,
self
.
_op_ids_to_skip
))
if
test_case_args
.
op_ids_to_skip
else
'none'
))
_logger
.
info
(
'---
FP32
prediction start ---'
)
_logger
.
info
(
'---
Quant
prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
),
batch_size
=
batch_size
)
fp32_output
,
fp32_acc1
,
fp32_acc5
,
fp32_fps
,
fp32
_lat
=
self
.
_predict
(
quant_output
,
quant_acc1
,
quant_acc5
,
quant_fps
,
quant
_lat
=
self
.
_predict
(
val_reader
,
fp32
_model_path
,
quant
_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
False
)
_logger
.
info
(
'--- Quant INT8 prediction start ---'
)
target
=
'quant'
)
self
.
_print_performance
(
'Quant'
,
quant_fps
,
quant_lat
)
self
.
_print_accuracy
(
'Quant'
,
quant_acc1
,
quant_acc5
)
_logger
.
info
(
'--- INT8 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
),
batch_size
=
batch_size
)
int8_output
,
int8_acc1
,
int8_acc5
,
int8_fps
,
int8_lat
=
self
.
_predict
(
...
...
@@ -355,11 +371,29 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
True
)
self
.
_summarize_performance
(
fp32_fps
,
fp32_lat
,
int8_fps
,
int8_lat
)
self
.
_compare_accuracy
(
fp32_acc1
,
fp32_acc5
,
int8_acc1
,
int8_acc5
,
acc_diff_threshold
)
target
=
'int8'
)
self
.
_print_performance
(
'INT8'
,
int8_fps
,
int8_lat
)
self
.
_print_accuracy
(
'INT8'
,
int8_acc1
,
int8_acc5
)
fp32_acc1
=
fp32_acc5
=
fp32_fps
=
fp32_lat
=
-
1
if
fp32_model_path
:
_logger
.
info
(
'--- FP32 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
),
batch_size
=
batch_size
)
fp32_output
,
fp32_acc1
,
fp32_acc5
,
fp32_fps
,
fp32_lat
=
self
.
_predict
(
val_reader
,
fp32_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
target
=
'fp32'
)
self
.
_print_performance
(
'FP32'
,
fp32_fps
,
fp32_lat
)
self
.
_print_accuracy
(
'FP32'
,
fp32_acc1
,
fp32_acc5
)
self
.
_summarize_performance
(
int8_fps
,
int8_lat
,
fp32_fps
,
fp32_lat
)
self
.
_summarize_accuracy
(
quant_acc1
,
quant_acc5
,
int8_acc1
,
int8_acc5
,
fp32_acc1
,
fp32_acc5
)
self
.
_compare_accuracy
(
acc_diff_threshold
,
quant_acc1
,
int8_acc1
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/contrib/slim/tests/quant2_int8_nlp_comparison.py
浏览文件 @
d0a921ba
...
...
@@ -17,8 +17,6 @@ import os
import
sys
import
argparse
import
logging
import
struct
import
six
import
numpy
as
np
import
time
import
paddle
...
...
@@ -143,7 +141,8 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
batch_size
=
1
,
batch_num
=
1
,
skip_batch_num
=
0
,
transform_to_int8
=
False
):
target
=
'quant'
):
assert
target
in
[
'quant'
,
'int8'
,
'fp32'
]
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
executor
.
global_scope
()
...
...
@@ -159,15 +158,19 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
graph
=
IrGraph
(
core
.
Graph
(
inference_program
.
desc
),
for_test
=
True
)
if
(
self
.
_debug
):
graph
.
draw
(
'.'
,
'quant_orig'
,
graph
.
all_op_nodes
())
if
(
t
ransform_to_int8
):
transform_to_mkldnn_int8
_pass
=
Quant2Int8MkldnnPass
(
if
(
t
arget
!=
'quant'
):
quant_transform
_pass
=
Quant2Int8MkldnnPass
(
self
.
_quantized_ops
,
_op_ids_to_skip
=
self
.
_op_ids_to_skip
,
_scope
=
inference_scope
,
_place
=
place
,
_core
=
core
,
_debug
=
self
.
_debug
)
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
if
(
target
==
'int8'
):
graph
=
quant_transform_pass
.
apply
(
graph
)
else
:
# target == fp32
graph
=
quant_transform_pass
.
prepare_and_optimize_fp32
(
graph
)
inference_program
=
graph
.
to_program
()
...
...
@@ -223,26 +226,35 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
return
acc_avg
,
pps_avg
,
latency_avg
def
_summarize_performance
(
self
,
fp32_pps
,
fp32_lat
,
int8_pps
,
int8_lat
):
_logger
.
info
(
'--- Performance summary ---'
)
_logger
.
info
(
'FP32: avg predictions per sec: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
fp32_pps
,
fp32_lat
))
def
_print_performance
(
self
,
title
,
pps
,
lat
):
_logger
.
info
(
'INT8: avg predictions per sec: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
int8_pps
,
int8_lat
))
'{0}: avg predictions per sec: {1:.2f}, avg latency: {2:.4f} ms'
.
format
(
title
,
pps
,
lat
))
def
_print_accuracy
(
self
,
title
,
acc
):
_logger
.
info
(
'{0}: avg accuracy: {1:.6f}'
.
format
(
title
,
acc
))
def
_summarize_performance
(
self
,
int8_pps
,
int8_lat
,
fp32_pps
,
fp32_lat
):
_logger
.
info
(
'--- Performance summary ---'
)
self
.
_print_performance
(
'INT8'
,
int8_pps
,
int8_lat
)
if
fp32_lat
>=
0
:
self
.
_print_performance
(
'FP32'
,
fp32_pps
,
fp32_lat
)
def
_
compare_accuracy
(
self
,
fp32_acc
,
int8_acc
,
threshold
):
def
_
summarize_accuracy
(
self
,
quant_acc
,
int8_acc
,
fp32_acc
):
_logger
.
info
(
'--- Accuracy summary ---'
)
self
.
_print_accuracy
(
'Quant'
,
quant_acc
)
self
.
_print_accuracy
(
'INT8'
,
int8_acc
)
if
fp32_acc
>=
0
:
self
.
_print_accuracy
(
'FP32'
,
fp32_acc
)
def
_compare_accuracy
(
self
,
threshold
,
quant_acc
,
int8_acc
):
_logger
.
info
(
'Accepted accuracy drop threshold: {0}. (condition: (
FP32
_acc - INT8_acc) <= threshold)'
'Accepted accuracy drop threshold: {0}. (condition: (
Quant
_acc - INT8_acc) <= threshold)'
.
format
(
threshold
))
_logger
.
info
(
'FP32: avg accuracy: {0:.6f}'
.
format
(
fp32_acc
))
_logger
.
info
(
'INT8: avg accuracy: {0:.6f}'
.
format
(
int8_acc
))
# Random outputs give accuracy about 0.33, we assume valid accuracy to be at least 0.5
assert
fp32
_acc
>
0.5
assert
quant
_acc
>
0.5
assert
int8_acc
>
0.5
assert
fp32
_acc
-
int8_acc
<=
threshold
assert
quant
_acc
-
int8_acc
<=
threshold
def
test_graph_transformation
(
self
):
if
not
fluid
.
core
.
is_compiled_with_mkldnn
():
...
...
@@ -250,9 +262,9 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
quant_model_path
=
test_case_args
.
quant_model
assert
quant_model_path
,
'The Quant model path cannot be empty. Please, use the --quant_model option.'
fp32_model_path
=
test_case_args
.
fp32_model
if
test_case_args
.
fp32_model
else
quant_model_path
data_path
=
test_case_args
.
infer_data
assert
data_path
,
'The dataset path cannot be empty. Please, use the --infer_data option.'
fp32_model_path
=
test_case_args
.
fp32_model
labels_path
=
test_case_args
.
labels
batch_size
=
test_case_args
.
batch_size
batch_num
=
test_case_args
.
batch_num
...
...
@@ -270,9 +282,10 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
self
.
_op_ids_to_skip
=
set
(
map
(
int
,
test_case_args
.
op_ids_to_skip
.
split
(
','
)))
_logger
.
info
(
'
FP32 & Quant
INT8 prediction run.'
)
_logger
.
info
(
'
Quant &
INT8 prediction run.'
)
_logger
.
info
(
'Quant model: {}'
.
format
(
quant_model_path
))
_logger
.
info
(
'FP32 model: {}'
.
format
(
fp32_model_path
))
if
fp32_model_path
:
_logger
.
info
(
'FP32 model: {}'
.
format
(
fp32_model_path
))
_logger
.
info
(
'Dataset: {}'
.
format
(
data_path
))
_logger
.
info
(
'Labels: {}'
.
format
(
labels_path
))
_logger
.
info
(
'Batch size: {}'
.
format
(
batch_size
))
...
...
@@ -284,18 +297,20 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
map
(
str
,
self
.
_op_ids_to_skip
))
if
test_case_args
.
op_ids_to_skip
else
'none'
))
_logger
.
info
(
'---
FP32
prediction start ---'
)
_logger
.
info
(
'---
Quant
prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
,
labels_path
),
batch_size
=
batch_size
)
fp32_acc
,
fp32_pps
,
fp32
_lat
=
self
.
_predict
(
quant_acc
,
quant_pps
,
quant
_lat
=
self
.
_predict
(
val_reader
,
fp32
_model_path
,
quant
_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
False
)
_logger
.
info
(
'FP32: avg accuracy: {0:.6f}'
.
format
(
fp32_acc
))
_logger
.
info
(
'--- Quant INT8 prediction start ---'
)
target
=
'quant'
)
self
.
_print_performance
(
'Quant'
,
quant_pps
,
quant_lat
)
self
.
_print_accuracy
(
'Quant'
,
quant_acc
)
_logger
.
info
(
'--- INT8 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
,
labels_path
),
batch_size
=
batch_size
)
int8_acc
,
int8_pps
,
int8_lat
=
self
.
_predict
(
...
...
@@ -304,11 +319,29 @@ class QuantInt8NLPComparisonTest(unittest.TestCase):
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
True
)
_logger
.
info
(
'INT8: avg accuracy: {0:.6f}'
.
format
(
int8_acc
))
target
=
'int8'
)
self
.
_print_performance
(
'INT8'
,
int8_pps
,
int8_lat
)
self
.
_print_accuracy
(
'INT8'
,
int8_acc
)
fp32_acc
=
fp32_pps
=
fp32_lat
=
-
1
if
fp32_model_path
:
_logger
.
info
(
'--- FP32 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
,
labels_path
),
batch_size
=
batch_size
)
fp32_acc
,
fp32_pps
,
fp32_lat
=
self
.
_predict
(
val_reader
,
fp32_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
target
=
'fp32'
)
self
.
_print_performance
(
'FP32'
,
fp32_pps
,
fp32_lat
)
self
.
_print_accuracy
(
'FP32'
,
fp32_acc
)
self
.
_summarize_performance
(
fp32_pps
,
fp32_lat
,
int8_pps
,
int8_lat
)
self
.
_compare_accuracy
(
fp32_acc
,
int8_acc
,
acc_diff_threshold
)
self
.
_summarize_performance
(
int8_pps
,
int8_lat
,
fp32_pps
,
fp32_lat
)
self
.
_summarize_accuracy
(
quant_acc
,
int8_acc
,
fp32_acc
)
self
.
_compare_accuracy
(
acc_diff_threshold
,
quant_acc
,
int8_acc
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/contrib/slim/tests/save_quant_model.py
浏览文件 @
d0a921ba
...
...
@@ -35,11 +35,6 @@ def parse_args():
type
=
str
,
default
=
''
,
help
=
'A path to a Quant model.'
)
parser
.
add_argument
(
'--fp32_model_save_path'
,
type
=
str
,
default
=
''
,
help
=
'Saved optimized fp32 model'
)
parser
.
add_argument
(
'--int8_model_save_path'
,
type
=
str
,
...
...
@@ -65,7 +60,7 @@ def parse_args():
return
test_args
,
sys
.
argv
[:
1
]
+
args
def
transform_and_save_
model
(
original_path
,
save_path
,
save_type
):
def
transform_and_save_
int8_model
(
original_path
,
save_path
):
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
executor
.
global_scope
()
...
...
@@ -96,26 +91,18 @@ def transform_and_save_model(original_path, save_path, save_type):
_place
=
place
,
_core
=
core
,
_debug
=
test_args
.
debug
)
graph
=
IrGraph
(
core
.
Graph
(
inference_program
.
desc
),
for_test
=
True
)
if
save_type
==
'FP32'
:
graph
=
transform_to_mkldnn_int8_pass
.
apply_fp32
(
graph
)
elif
save_type
==
'INT8'
:
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
inference_program
=
graph
.
to_program
()
with
fluid
.
scope_guard
(
inference_scope
):
fluid
.
io
.
save_inference_model
(
save_path
,
feed_target_names
,
fetch_targets
,
exe
,
inference_program
)
print
(
"Success! Transformed Quant_{0} model can be found at {1}
\n
"
.
format
(
save_type
,
save_path
))
print
(
"Success! INT8 model obtained from the Quant model can be found at {}
\n
"
.
format
(
save_path
))
if
__name__
==
'__main__'
:
global
test_args
test_args
,
remaining_args
=
parse_args
()
if
test_args
.
fp32_model_save_path
:
transform_and_save_model
(
test_args
.
quant_model_path
,
test_args
.
fp32_model_save_path
,
'FP32'
)
if
test_args
.
int8_model_save_path
:
transform_and_save_model
(
test_args
.
quant_model_path
,
test_args
.
int8_model_save_path
,
'INT8'
)
transform_and_save_int8_model
(
test_args
.
quant_model_path
,
test_args
.
int8_model_save_path
)
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