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001dab0b
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体验新版 GitCode,发现更多精彩内容 >>
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001dab0b
编写于
5月 06, 2022
作者:
A
Allen Guo
提交者:
GitHub
5月 06, 2022
浏览文件
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差异文件
update UTs 2 (#42518)
上级
063a3509
变更
19
隐藏空白更改
内联
并排
Showing
19 changed file
with
845 addition
and
1154 deletion
+845
-1154
python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py
+17
-58
python/paddle/fluid/tests/unittests/ipu/test_mixed_precision_inference_ipu.py
...tests/unittests/ipu/test_mixed_precision_inference_ipu.py
+120
-84
python/paddle/fluid/tests/unittests/ipu/test_mixed_precision_training_ipu.py
.../tests/unittests/ipu/test_mixed_precision_training_ipu.py
+134
-92
python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py
+19
-62
python/paddle/fluid/tests/unittests/ipu/test_not_equal_op_ipu.py
...paddle/fluid/tests/unittests/ipu/test_not_equal_op_ipu.py
+130
-0
python/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.py
...n/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.py
+21
-61
python/paddle/fluid/tests/unittests/ipu/test_one_hot_v2_op_ipu.py
...addle/fluid/tests/unittests/ipu/test_one_hot_v2_op_ipu.py
+21
-61
python/paddle/fluid/tests/unittests/ipu/test_optimizer_ipu.py
...on/paddle/fluid/tests/unittests/ipu/test_optimizer_ipu.py
+0
-2
python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
+33
-58
python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
+33
-58
python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py
+25
-106
python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py
+33
-67
python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py
+15
-56
python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py
.../fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py
+18
-59
python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py
...n/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py
+17
-58
python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py
+25
-102
python/paddle/fluid/tests/unittests/ipu/test_scaled_optimizer_state_ipu.py
...id/tests/unittests/ipu/test_scaled_optimizer_state_ipu.py
+131
-0
python/paddle/fluid/tests/unittests/ipu/test_set_batch_size_ipu.py
...ddle/fluid/tests/unittests/ipu/test_set_batch_size_ipu.py
+25
-64
python/paddle/fluid/tests/unittests/ipu/test_slice_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_slice_op_ipu.py
+28
-106
未找到文件。
python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -46,59 +42,22 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
mean
(
x
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
mean
(
x
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_mixed_precision_inference_ipu.py
浏览文件 @
001dab0b
...
...
@@ -18,7 +18,7 @@ import numpy as np
import
paddle
import
paddle.static
import
paddle.nn.functional
as
F
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionModeFull
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -28,10 +28,7 @@ class TestBase(IPUOpTest):
self
.
set_atol
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
@
property
def
fp16_enabled
(
self
):
return
True
self
.
set_attrs
()
def
set_atol
(
self
):
self
.
atol
=
1e-6
...
...
@@ -42,7 +39,6 @@ class TestBase(IPUOpTest):
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
10
,
27
,
27
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"in_0"
:
data
.
astype
(
np
.
float16
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
...
...
@@ -54,86 +50,126 @@ class TestBase(IPUOpTest):
for
var_name
in
to_fp16_var_names
:
assert
(
block
.
var
(
var_name
).
dtype
,
paddle
.
float16
)
def
_test_base
(
self
,
exec_mode
):
generator
=
paddle
.
fluid
.
unique_name
.
UniqueNameGenerator
()
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
fluid
.
unique_name
.
guard
(
generator
):
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
fetch_list
=
[
loss
.
name
]
if
exec_mode
==
ExecutionModeFull
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
# cast model to fp16
if
exec_mode
==
ExecutionModeFull
.
IPU_MIXED_PRECISION
:
to_fp16_var_names
=
paddle
.
static
.
amp
.
cast_model_to_fp16
(
main_prog
,
self
.
amp_list
)
self
.
dtype_check
(
main_prog
,
to_fp16_var_names
)
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
# cast parameters to fp16
if
exec_mode
==
ExecutionModeFull
.
IPU_MIXED_PRECISION
:
paddle
.
static
.
amp
.
cast_parameters_to_fp16
(
paddle
.
CPUPlace
(),
main_prog
,
to_fp16_var_names
=
to_fp16_var_names
)
if
exec_mode
!=
ExecutionModeFull
.
CPU_FP32
:
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
False
)
if
exec_mode
==
ExecutionModeFull
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
self
.
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
set_attrs
(
self
):
self
.
num_ipus
=
1
self
.
enable_pipelining
=
False
self
.
enable_manual_shard
=
False
self
.
batches_per_step
=
1
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
self
.
fetch_list
=
[
loss
.
name
]
def
run_model
(
self
,
exec_mode
):
# cast model to fp16
if
self
.
is_fp16_mode
(
exec_mode
):
amp_list
=
paddle
.
static
.
amp
.
CustomOpLists
()
amp_list
.
unsupported_list
=
{}
to_fp16_var_names
=
paddle
.
static
.
amp
.
cast_model_to_fp16
(
self
.
main_prog
,
amp_list
,
use_fp16_guard
=
True
)
self
.
dtype_check
(
self
.
main_prog
,
to_fp16_var_names
)
if
self
.
is_ipu_mode
(
exec_mode
):
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
self
.
startup_prog
)
# cast parameters to fp16
if
exec_mode
==
IPUOpTest
.
ExecutionMode
.
IPU_FP16
:
paddle
.
static
.
amp
.
cast_parameters_to_fp16
(
paddle
.
CPUPlace
(),
self
.
main_prog
,
to_fp16_var_names
=
to_fp16_var_names
)
if
self
.
is_ipu_mode
(
exec_mode
):
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
False
,
num_ipus
=
self
.
num_ipus
,
enable_manual_shard
=
self
.
enable_manual_shard
)
ipu_strategy
.
set_pipelining_config
(
enable_pipelining
=
self
.
enable_pipelining
,
batches_per_step
=
self
.
batches_per_step
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
self
.
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
self
.
feed_list
,
self
.
fetch_list
)
else
:
program
=
self
.
main_prog
result
=
exe
.
run
(
program
,
feed
=
self
.
feed_fp32
,
fetch_list
=
self
.
fetch_list
)
self
.
output_dict
[
exec_mode
]
=
result
[
0
]
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestPipline
(
TestBase
):
@
IPUOpTest
.
static_graph
def
build_model
(
self
,
exec_mode
):
feed_shape
=
list
(
self
.
feed_shape
[
0
])
if
self
.
is_ipu_mode
(
exec_mode
):
feed_shape
[
0
]
=
1
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
feed_shape
,
dtype
=
'float32'
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
0
,
stage
=
0
):
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
1
,
stage
=
1
):
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
2
,
stage
=
2
):
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
self
.
fetch_list
=
[
loss
.
name
]
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
3
,
10
,
27
,
27
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
def
set_attrs
(
self
):
self
.
num_ipus
=
3
self
.
enable_pipelining
=
True
self
.
enable_manual_shard
=
True
self
.
batches_per_step
=
3
def
test
(
self
):
output_dict
=
{}
for
mode
in
ExecutionModeFull
:
if
mode
==
ExecutionModeFull
.
IPU_POPART_FP16
:
continue
if
mode
>
ExecutionModeFull
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
for
m
in
IPUOpTest
.
ExecutionMode
:
self
.
build_model
(
m
)
self
.
run_model
(
m
)
# skip check results
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_mixed_precision_training_ipu.py
浏览文件 @
001dab0b
...
...
@@ -18,7 +18,7 @@ import numpy as np
import
paddle
import
paddle.static
import
paddle.nn.functional
as
F
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionModeFull
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -29,10 +29,7 @@ class TestBase(IPUOpTest):
self
.
set_training
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
@
property
def
fp16_enabled
(
self
):
return
True
self
.
set_attrs
()
def
set_atol
(
self
):
self
.
atol
=
2e-6
...
...
@@ -47,104 +44,149 @@ class TestBase(IPUOpTest):
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
28
,
28
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"in_0"
:
data
.
astype
(
np
.
float16
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_fp32
.
keys
())
def
set_attrs
(
self
):
self
.
num_ipus
=
1
self
.
enable_pipelining
=
False
self
.
enable_manual_shard
=
False
self
.
batches_per_step
=
1
def
dtype_check
(
self
,
program
,
to_fp16_var_names
):
block
=
program
.
global_block
()
assert
len
(
to_fp16_var_names
)
>
0
for
var_name
in
to_fp16_var_names
:
assert
(
block
.
var
(
var_name
).
dtype
,
paddle
.
float16
)
def
_test_base
(
self
,
exec_mode
):
generator
=
paddle
.
fluid
.
unique_name
.
UniqueNameGenerator
()
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
fluid
.
unique_name
.
guard
(
generator
):
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
# optimizer
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
optimizer
.
minimize
(
loss
,
startup_prog
)
fetch_list
=
[
loss
.
name
]
# cast model to fp16
if
exec_mode
==
ExecutionModeFull
.
IPU_MIXED_PRECISION
:
to_fp16_var_names
=
paddle
.
static
.
amp
.
cast_model_to_fp16
(
main_prog
,
self
.
amp_list
)
self
.
dtype_check
(
main_prog
,
to_fp16_var_names
)
if
exec_mode
==
ExecutionModeFull
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
# cast parameters to fp16
if
exec_mode
==
ExecutionModeFull
.
IPU_MIXED_PRECISION
:
paddle
.
static
.
amp
.
cast_parameters_to_fp16
(
paddle
.
CPUPlace
(),
main_prog
,
to_fp16_var_names
=
to_fp16_var_names
)
if
exec_mode
!=
ExecutionModeFull
.
CPU_FP32
:
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionModeFull
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
self
.
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
result
=
[]
for
i
in
range
(
self
.
epoch
):
out
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
out
)
return
np
.
array
(
result
)
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionModeFull
:
if
mode
==
ExecutionModeFull
.
IPU_POPART_FP16
:
continue
if
mode
>
ExecutionModeFull
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
# optimizer
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
optimizer
.
minimize
(
loss
,
self
.
startup_prog
)
self
.
fetch_list
=
[
loss
.
name
]
def
run_model
(
self
,
exec_mode
):
# cast model to fp16
if
self
.
is_fp16_mode
(
exec_mode
):
amp_list
=
paddle
.
static
.
amp
.
CustomOpLists
()
amp_list
.
unsupported_list
=
{}
to_fp16_var_names
=
paddle
.
static
.
amp
.
cast_model_to_fp16
(
self
.
main_prog
,
amp_list
)
self
.
dtype_check
(
self
.
main_prog
,
to_fp16_var_names
)
if
self
.
is_ipu_mode
(
exec_mode
):
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
self
.
startup_prog
)
# cast parameters to fp16
if
self
.
is_fp16_mode
(
exec_mode
):
paddle
.
static
.
amp
.
cast_parameters_to_fp16
(
paddle
.
CPUPlace
(),
self
.
main_prog
,
to_fp16_var_names
=
to_fp16_var_names
)
if
self
.
is_ipu_mode
(
exec_mode
):
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
,
num_ipus
=
self
.
num_ipus
,
enable_manual_shard
=
self
.
enable_manual_shard
)
ipu_strategy
.
set_pipelining_config
(
enable_pipelining
=
self
.
enable_pipelining
,
batches_per_step
=
self
.
batches_per_step
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
self
.
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
self
.
feed_list
,
self
.
fetch_list
)
else
:
program
=
self
.
main_prog
result
=
[]
for
_
in
range
(
self
.
epoch
):
out
=
exe
.
run
(
program
,
feed
=
self
.
feed_fp32
,
fetch_list
=
self
.
fetch_list
)
result
.
append
(
out
)
self
.
output_dict
[
exec_mode
]
=
result
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestPipline
(
TestBase
):
@
IPUOpTest
.
static_graph
def
build_model
(
self
,
exec_mode
):
feed_shape
=
list
(
self
.
feed_shape
[
0
])
if
self
.
is_ipu_mode
(
exec_mode
):
feed_shape
[
0
]
=
1
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
feed_shape
,
dtype
=
'float32'
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
0
,
stage
=
0
):
# using fp32
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
3
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
1
,
stage
=
1
):
# using fp16
with
paddle
.
static
.
amp
.
fp16_guard
():
x
=
paddle
.
static
.
nn
.
conv2d
(
input
=
x
,
num_filters
=
6
,
filter_size
=
3
)
x
=
paddle
.
static
.
nn
.
batch_norm
(
x
,
act
=
'relu'
)
x
=
F
.
max_pool2d
(
x
,
kernel_size
=
2
,
stride
=
2
)
with
paddle
.
static
.
ipu_shard_guard
(
index
=
2
,
stage
=
2
):
# using fp32
x
=
paddle
.
static
.
nn
.
fc
(
x
,
size
=
10
)
loss
=
paddle
.
mean
(
x
)
# optimizer
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
optimizer
.
minimize
(
loss
,
self
.
startup_prog
)
self
.
fetch_list
=
[
loss
.
name
]
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
5
,
10
,
27
,
27
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
def
set_attrs
(
self
):
self
.
num_ipus
=
3
self
.
enable_pipelining
=
True
self
.
enable_manual_shard
=
True
self
.
batches_per_step
=
5
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
self
.
build_model
(
m
)
self
.
run_model
(
m
)
# skip check results
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
x
=
np
.
random
.
uniform
(
size
=
[
2
,
5
])
y
=
np
.
random
.
uniform
(
size
=
[
5
,
3
])
...
...
@@ -51,63 +47,24 @@ class TestBase(IPUOpTest):
"y_num_col_dims"
:
1
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
mul
(
x
,
y
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
mul
(
x
,
y
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_not_equal_op_ipu.py
0 → 100644
浏览文件 @
001dab0b
# Copyright (c) 2022 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.
import
unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestBase
(
IPUOpTest
):
def
setUp
(
self
):
self
.
set_atol
()
self
.
set_training
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
self
.
set_op_attrs
()
def
set_data_feed
(
self
):
x
=
np
.
ones
([
1
,
10
])
y
=
np
.
zeros
([
1
,
10
])
self
.
feed_fp32
=
{
"x"
:
x
.
astype
(
np
.
float32
),
"y"
:
y
.
astype
(
np
.
float32
),
}
self
.
feed_fp16
=
{
"x"
:
x
.
astype
(
np
.
float16
),
"y"
:
y
.
astype
(
np
.
float16
),
}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_fp32
.
keys
())
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
not_equal
(
x
,
y
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
def
set_data_feed
(
self
):
x
=
np
.
ones
([
1
,
10
])
y
=
np
.
ones
([
1
,
10
])
self
.
feed_fp32
=
{
"x"
:
x
.
astype
(
np
.
float32
),
"y"
:
y
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"x"
:
x
.
astype
(
np
.
float16
),
"y"
:
y
.
astype
(
np
.
float16
)}
class
TestCase2
(
TestBase
):
def
set_data_feed
(
self
):
x
=
np
.
ones
([
1
,
10
])
y
=
np
.
arange
(
0
,
10
).
reshape
([
1
,
10
])
self
.
feed_fp32
=
{
"x"
:
x
.
astype
(
np
.
float32
),
"y"
:
y
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"x"
:
x
.
astype
(
np
.
float16
),
"y"
:
y
.
astype
(
np
.
float16
)}
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestScalar
(
IPUOpTest
):
def
setUp
(
self
):
self
.
set_atol
()
self
.
set_training
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
self
.
set_op_attrs
()
def
set_data_feed
(
self
):
x
=
np
.
ones
([
1
,
10
])
y
=
0.5
self
.
feed_fp32
=
{
"x"
:
x
.
astype
(
np
.
float32
),
}
self
.
feed_fp16
=
{
"x"
:
x
.
astype
(
np
.
float16
),
}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_fp32
.
keys
())
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
(
x
!=
0.5
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,74 +30,34 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data1
=
np
.
array
([[
1
],
[
1
],
[
3
],
[
0
]])
self
.
feed
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
self
.
feed_fp32
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
self
.
feed
_fp16
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
.
keys
())
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
_fp32
.
keys
())
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"depth"
:
4
,
"allow_out_of_range"
:
False
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
one_hot
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
(
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
):
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
one_hot
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
@
unittest
.
skip
(
'does not support allow_out_of_range=True'
)
...
...
python/paddle/fluid/tests/unittests/ipu/test_one_hot_v2_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,74 +30,34 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data1
=
np
.
array
([[
1
],
[
1
],
[
3
],
[
0
]])
self
.
feed
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
self
.
feed_fp32
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
self
.
feed
_fp16
=
{
'x'
:
data1
.
astype
(
np
.
int32
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
.
keys
())
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
_fp32
.
keys
())
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"depth"
:
4
,
"allow_out_of_range"
:
False
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
input
.
one_hot
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
(
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
):
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
input
.
one_hot
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
@
unittest
.
skip
(
'does not support allow_out_of_range=True'
)
...
...
python/paddle/fluid/tests/unittests/ipu/test_optimizer_ipu.py
浏览文件 @
001dab0b
...
...
@@ -12,8 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
numpy
as
np
import
unittest
import
paddle
...
...
python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
])
self
.
feed_fp32
=
{
'in_0'
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -56,59 +52,22 @@ class TestBase(IPUOpTest):
"data_format"
:
'NCHW'
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
@@ -180,5 +139,21 @@ class TestCase6(TestBase):
self
.
attrs
[
'exclusive'
]
=
False
class
TestAdaptive
(
TestBase
):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"pool_size"
:
1
,
"pool_type"
:
'avg'
,
"require_index"
:
False
}
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
adaptive_pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
])
self
.
feed_fp32
=
{
'in_0'
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -56,59 +52,22 @@ class TestBase(IPUOpTest):
"data_format"
:
'NCHW'
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
@@ -179,5 +138,21 @@ class TestCase6(TestBase):
self
.
attrs
[
'exclusive'
]
=
False
class
TestAdaptive
(
TestBase
):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"pool_size"
:
1
,
"pool_type"
:
'max'
,
"require_index"
:
False
}
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
adaptive_pool2d
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
2
,
2
])
self
.
feed_fp32
=
{
"x"
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -47,59 +43,22 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"factor"
:
2.0
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pow
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pow
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
@@ -119,54 +78,14 @@ class TestCase1(TestBase):
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
factor
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pow
(
x
,
factor
=
factor
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
factor
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
pow
(
x
,
factor
=
factor
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -30,82 +30,48 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
False
def
set_data_feed
(
self
):
self
.
feed
=
{
"x"
:
np
.
random
.
uniform
(
size
=
[
1
,
3
,
3
,
3
]).
astype
(
'float32'
),
}
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
3
,
3
]).
astype
(
'float32'
)
self
.
feed_fp32
=
{
"x"
:
data
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"x"
:
data
.
astype
(
np
.
float16
)
}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed
.
values
()]
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed
_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed
_fp32
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed
_fp32
.
values
()]
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
run_ipu
=
True
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
self
.
feed_dtype
[
0
])
out
=
paddle
.
fluid
.
layers
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
)
out
=
paddle
.
fluid
.
layers
.
Print
(
out
,
**
self
.
attrs
)
if
self
.
is_training
:
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
fetch_list
=
[
loss
.
name
]
else
:
fetch_list
=
[
out
.
name
]
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
run_ipu
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
if
self
.
is_training
:
result
=
[]
for
_
in
range
(
self
.
epoch
):
loss_res
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
[
0
])
return
np
.
array
(
result
)
else
:
result
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
self
.
feed_dtype
[
0
])
out
=
paddle
.
fluid
.
layers
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
)
out
=
paddle
.
fluid
.
layers
.
Print
(
out
,
**
self
.
attrs
)
if
self
.
is_training
:
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
self
.
fetch_list
=
[
loss
.
name
]
else
:
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
res0
=
self
.
_test_base
(
False
)
res1
=
self
.
_test_base
(
True
)
self
.
assertTrue
(
np
.
allclose
(
res0
.
flatten
(),
res1
.
flatten
(),
atol
=
self
.
atol
))
self
.
assertTrue
(
res0
.
shape
==
res1
.
shape
)
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -28,10 +28,6 @@ class TestMean(IPUOpTest):
self
.
set_training
()
self
.
set_test_op
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_test_op
(
self
):
self
.
op
=
paddle
.
fluid
.
layers
.
reduce_mean
...
...
@@ -40,59 +36,22 @@ class TestMean(IPUOpTest):
self
.
feed_list
=
list
(
self
.
feed_fp32
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed_fp32
.
values
()]
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
self
.
op
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
self
.
op
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
self
.
check
(
output_dict
)
def
run_test_base
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
def
set_data_feed0
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
2
,
4
])
...
...
python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
])
self
.
feed_fp32
=
{
"x"
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -50,60 +46,23 @@ class TestBase(IPUOpTest):
"inplace"
:
True
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
add
=
paddle
.
fluid
.
layers
.
elementwise_add
(
x
,
x
)
out
=
paddle
.
fluid
.
layers
.
reshape
(
add
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
)
self
.
check
(
output_dict
,
check_shape
=
True
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
add
=
paddle
.
fluid
.
layers
.
elementwise_add
(
x
,
x
)
out
=
paddle
.
fluid
.
layers
.
reshape
(
add
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
2
,
4
,
6
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -48,59 +44,22 @@ class TestBase(IPUOpTest):
self
.
attrs
[
'shape'
]
=
[
6
,
8
]
self
.
attrs
[
'inplace'
]
=
False
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
reshape
(
x
=
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
)
self
.
check
(
output_dict
,
check_shape
=
True
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
reshape
(
x
=
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -51,59 +51,22 @@ class TestBase(IPUOpTest):
"bias_after_scale"
:
True
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
scale
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
scale
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
@@ -155,54 +118,14 @@ class TestCase5(TestBase):
"bias_after_scale"
:
True
,
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
scale
(
x
,
scale
=
y
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
scale
(
x
,
scale
=
y
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_scaled_optimizer_state_ipu.py
0 → 100644
浏览文件 @
001dab0b
# Copyright (c) 2022 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.
import
numpy
as
np
import
unittest
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestBase
(
IPUOpTest
):
def
setUp
(
self
):
self
.
set_atol
()
self
.
set_training
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
self
.
set_attrs
()
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
100
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
]).
astype
(
'float32'
)
self
.
feed_fp32
=
{
"image"
:
data
.
astype
(
np
.
float32
)}
self
.
feed_fp16
=
{
"image"
:
data
.
astype
(
np
.
float16
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_fp32
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed_fp32
.
values
()]
def
set_attrs
(
self
):
self
.
attrs
=
{
"optimizer"
:
'lamb'
,
"weight_decay"
:
0.0
,
"scaled_optimizer_state"
:
True
}
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
image
=
paddle
.
static
.
data
(
name
=
'image'
,
shape
=
[
1
,
3
,
10
,
10
],
dtype
=
'float32'
)
conv1
=
paddle
.
static
.
nn
.
conv2d
(
image
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
loss
=
paddle
.
mean
(
conv1
)
weight_decay
=
self
.
attrs
[
'weight_decay'
]
opt
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-1
,
weight_decay
=
weight_decay
)
if
self
.
attrs
[
'optimizer'
]
==
'lamb'
:
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-1
,
lamb_weight_decay
=
weight_decay
)
opt
.
minimize
(
loss
)
self
.
feed_list
=
[
image
.
name
]
self
.
fetch_list
=
[
loss
.
name
]
def
run_model
(
self
,
exec_mode
):
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
self
.
is_ipu_mode
(
exec_mode
):
if
"use_no_bias_optimizer"
in
self
.
attrs
.
keys
():
ipu_strategy
.
set_options
({
"use_no_bias_optimizer"
:
self
.
attrs
[
"use_no_bias_optimizer"
]
})
if
"scaled_optimizer_state"
in
self
.
attrs
.
keys
():
ipu_strategy
.
set_options
({
"scaled_optimizer_state"
:
self
.
attrs
[
"scaled_optimizer_state"
]
})
self
.
run_op_test
(
exec_mode
,
ipu_strategy
=
ipu_strategy
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestScaledAdam
(
TestBase
):
def
set_attrs
(
self
):
self
.
attrs
=
{
"optimizer"
:
'adam'
,
"weight_decay"
:
0.0
,
"scaled_optimizer_state"
:
True
}
def
set_atol
(
self
):
super
().
set_atol
()
self
.
atol
=
1e-5
self
.
rtol
=
1e-5
@
unittest
.
skip
(
'cpu do not support AdamNoBias'
)
class
TestScaledAdamNoBias
(
TestBase
):
def
set_attrs
(
self
):
self
.
attrs
=
{
"optimizer"
:
'adam'
,
"weight_decay"
:
0.0
,
"use_no_bias_optimizer"
:
True
,
"scaled_optimizer_state"
:
True
}
@
unittest
.
skip
(
'cpu do not support LambNoBias'
)
class
TestScaledLambNoBias
(
TestBase
):
def
set_attrs
(
self
):
self
.
attrs
=
{
"optimizer"
:
'lamb'
,
"weight_decay"
:
0.0
,
"use_no_bias_optimizer"
:
True
,
"scaled_optimizer_state"
:
True
}
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_set_batch_size_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_atol
(
self
):
self
.
atol
=
3e-6
self
.
rtol
=
1e-5
...
...
@@ -52,67 +48,32 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv2
=
paddle
.
static
.
nn
.
conv2d
(
conv1
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv3
=
paddle
.
static
.
nn
.
conv2d
(
conv2
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv4
=
paddle
.
static
.
nn
.
conv2d
(
conv3
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
fetch_list
=
[
conv4
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
,
micro_batch_size
=
2
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv2
=
paddle
.
static
.
nn
.
conv2d
(
conv1
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv3
=
paddle
.
static
.
nn
.
conv2d
(
conv2
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv4
=
paddle
.
static
.
nn
.
conv2d
(
conv3
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
self
.
fetch_list
=
[
conv4
.
name
]
def
run_model
(
self
,
exec_mode
):
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
,
micro_batch_size
=
2
)
self
.
run_op_test
(
exec_mode
,
ipu_strategy
)
def
test
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
).
flatten
()
self
.
check
(
output_dict
)
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_slice_op_ipu.py
浏览文件 @
001dab0b
...
...
@@ -17,7 +17,7 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.static
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
,
ExecutionMode
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
...
...
@@ -30,10 +30,6 @@ class TestBase(IPUOpTest):
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
4
,
5
,
6
])
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
np
.
float32
)}
...
...
@@ -51,59 +47,22 @@ class TestBase(IPUOpTest):
"ends"
:
[
3
,
2
,
4
],
}
def
_test_base
(
self
,
exec_mode
):
scope
=
paddle
.
static
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
slice
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
!=
ExecutionMode
.
CPU_FP32
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
if
exec_mode
==
ExecutionMode
.
IPU_POPART_FP16
:
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
feed
=
self
.
feed_fp32
if
exec_mode
>
ExecutionMode
.
IPU_FP32
:
feed
=
self
.
feed_fp16
result
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
output_dict
[
mode
]
=
self
.
_test_base
(
mode
)
self
.
check
(
output_dict
,
check_shape
=
True
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
slice
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
class
TestCase1
(
TestBase
):
...
...
@@ -135,54 +94,17 @@ class TestCase2(TestBase):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"axes"
:
[
0
,
1
,
2
]}
def
_test_base
(
self
,
run_ipu
=
True
):
scope
=
fluid
.
core
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
self
.
SEED
startup_prog
.
random_seed
=
self
.
SEED
with
fluid
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
starts
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'int32'
)
ends
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
2
],
shape
=
self
.
feed_shape
[
2
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
slice
(
x
,
starts
=
starts
,
ends
=
ends
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
run_ipu
:
feed_list
=
self
.
feed_list
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
self
.
is_training
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
result
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
pass
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
starts
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'int32'
)
ends
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
2
],
shape
=
self
.
feed_shape
[
2
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
slice
(
x
,
starts
=
starts
,
ends
=
ends
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
...
...
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