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体验新版 GitCode,发现更多精彩内容 >>
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063a3509
编写于
5月 06, 2022
作者:
A
Allen Guo
提交者:
GitHub
5月 06, 2022
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差异文件
update UTs 1 (#42517)
上级
63d4d05a
变更
19
隐藏空白更改
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并排
Showing
19 changed file
with
487 addition
and
1501 deletion
+487
-1501
python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py
...e/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py
+0
-160
python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py
...paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py
+35
-145
python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py
...on/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py
+16
-59
python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py
python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py
+14
-55
python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py
...addle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py
+51
-72
python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py
...n/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py
+17
-60
python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py
...paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py
+33
-87
python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py
...dle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py
+30
-83
python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py
...on/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py
+0
-111
python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py
...paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py
+47
-108
python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py
...ddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py
+14
-55
python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py
...ddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py
+23
-59
python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py
...paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.py
+24
-55
python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py
...ddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py
+28
-78
python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py
...e/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py
+29
-78
python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py
.../paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py
+48
-62
python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py
...on/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py
+20
-62
python/paddle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py
...dle/fluid/tests/unittests/ipu/test_matmul_serilize_ipu.py
+39
-50
python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py
...paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py
+19
-62
未找到文件。
python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_ipu.py
已删除
100644 → 0
浏览文件 @
63d4d05a
# 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
shutil
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
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_data_feed
()
self
.
set_feed_attr
()
self
.
set_op_attrs
()
def
set_atol
(
self
):
self
.
atol
=
1e-6
self
.
rtol
=
1e-5
self
.
atol_fp16
=
1e-2
self
.
rtol_fp16
=
1e-3
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
])
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_op_attrs
(
self
):
self
.
attrs
=
{}
self
.
attrs
[
'steps'
]
=
100
self
.
attrs
[
'save_at_step'
]
=
20
self
.
attrs
[
'is_training'
]
=
True
self
.
attrs
[
'opt_type'
]
=
'sgd'
self
.
attrs
[
'path'
]
=
'model'
self
.
attrs
[
'model_name'
]
=
'test'
def
_test_save
(
self
):
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
generator
=
paddle
.
fluid
.
unique_name
.
UniqueNameGenerator
()
self
.
full_name
=
'/'
.
join
(
[
self
.
attrs
[
'path'
],
self
.
attrs
[
'model_name'
]])
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'
)
scale
=
paddle
.
fluid
.
layers
.
scale
(
x
,
scale
=
1.0
,
bias
=
0.0
,
bias_after_scale
=
True
)
conv
=
paddle
.
static
.
nn
.
conv2d
(
scale
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
,
name
=
'conv2d'
)
loss
=
paddle
.
mean
(
conv
)
if
self
.
attrs
[
'is_training'
]:
if
self
.
attrs
[
'opt_type'
]
==
'sgd'
:
sgd
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
1e-2
)
sgd
.
minimize
(
loss
)
elif
self
.
attrs
[
'opt_type'
]
==
'adam'
:
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
elif
self
.
attrs
[
'opt_type'
]
==
'lamb'
:
lamb
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-2
)
lamb
.
minimize
(
loss
)
fetch_list
=
[
loss
.
name
]
place
=
paddle
.
IPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
True
)
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
self
.
feed_list
,
fetch_list
)
for
_
in
range
(
self
.
attrs
[
'steps'
]):
exe
.
run
(
program
,
feed
=
self
.
feed_fp16
,
fetch_list
=
fetch_list
)
paddle
.
static
.
save_inference_model
(
self
.
full_name
,
x
,
loss
,
exe
,
program
=
program
.
org_program
)
def
_test_load
(
self
,
run_ipu
):
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
(
paddle
.
static
.
load_inference_model
(
self
.
full_name
,
exe
))
if
run_ipu
:
feed_list
=
feed_target_names
fetch_list
=
[
fetch_targets
[
0
].
name
]
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
False
)
ipu_strategy
.
set_precision_config
(
enable_fp16
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
inference_program
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
inference_program
feed
=
self
.
feed_fp16
if
run_ipu
else
self
.
feed_fp32
result
=
[]
for
i
in
range
(
10
):
feed
[
"in_0"
]
+=
np
.
array
([
1.1
*
i
]).
astype
(
feed
[
"in_0"
].
dtype
)
out
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
[
fetch_targets
])
result
.
append
(
out
)
return
np
.
array
(
result
)
def
test_base
(
self
):
self
.
_test_save
()
cpu_res
=
self
.
_test_load
(
False
)
ipu_res
=
self
.
_test_load
(
True
).
astype
(
np
.
float32
)
self
.
assertTrue
(
np
.
allclose
(
cpu_res
,
ipu_res
,
rtol
=
self
.
rtol_fp16
,
atol
=
self
.
atol_fp16
))
shutil
.
rmtree
(
self
.
attrs
[
'path'
],
True
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_fp16_support_ipu.py
浏览文件 @
063a3509
...
...
@@ -16,9 +16,8 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
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
(),
...
...
@@ -31,10 +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
=
5e-6
self
.
rtol
=
1e-5
...
...
@@ -54,80 +49,32 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
exec_mode
):
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'
)
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
conv2
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
add1
=
conv1
+
conv2
conv3
=
paddle
.
static
.
nn
.
conv2d
(
add1
,
num_filters
=
8
,
filter_size
=
8
,
bias_attr
=
False
)
out
=
paddle
.
fluid
.
layers
.
relu
(
conv3
,
**
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
)
class
TestIntInput
(
IPUOpTest
):
def
setUp
(
self
):
self
.
set_atol
()
self
.
set_training
()
self
.
set_data_feed
()
self
.
set_feed_attr
()
self
.
set_op_attrs
()
@
property
def
fp16_enabled
(
self
):
return
True
@
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
(
x
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
add1
=
conv1
+
conv2
conv3
=
paddle
.
static
.
nn
.
conv2d
(
add1
,
num_filters
=
8
,
filter_size
=
8
,
bias_attr
=
False
)
out
=
paddle
.
fluid
.
layers
.
relu
(
conv3
,
**
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
TestIntInput
(
TestBase
):
def
set_data_feed
(
self
):
embedding
=
np
.
random
.
uniform
(
size
=
[
10
,
20
])
indice
=
np
.
array
([
1
,
3
,
5
]).
astype
(
np
.
int32
)
...
...
@@ -140,71 +87,14 @@ class TestIntInput(IPUOpTest):
"indice"
:
indice
,
}
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_op_attrs
(
self
):
self
.
attrs
=
{}
def
_test_base
(
self
,
exec_mode
):
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'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
gather
(
x
,
index
=
y
)
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
np
.
array
(
result
)
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
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
gather
(
x
,
index
=
y
)
self
.
fetch_list
=
[
out
.
name
]
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_gather_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
=
[
10
,
20
])
y
=
np
.
array
([
1
,
3
,
5
])
...
...
@@ -47,63 +43,24 @@ 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'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
gather
(
x
,
index
=
y
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
@
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
=
'int32'
)
out
=
paddle
.
fluid
.
layers
.
gather
(
x
,
index
=
y
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
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
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
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
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_gelu_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
=
{
"approximate"
:
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
.
gelu
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
@
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
.
gelu
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
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
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
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
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_gradient_clip_ipu.py
浏览文件 @
063a3509
...
...
@@ -28,19 +28,26 @@ class TestBase(IPUOpTest):
self
.
set_data_feed
()
self
.
set_feed_attr
()
self
.
set_attrs
()
self
.
set_training
()
@
property
def
fp16_enabled
(
self
):
return
False
def
set_atol
(
self
):
super
().
set_atol
()
self
.
atol
=
1e-6
self
.
rtol
=
1e-5
def
set_data_feed
(
self
):
self
.
feed
=
{
self
.
feed
_fp32
=
{
"image"
:
np
.
random
.
uniform
(
size
=
[
1
,
3
,
10
,
10
]).
astype
(
'float32'
),
}
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_attrs
(
self
):
self
.
attrs
=
{
...
...
@@ -48,76 +55,48 @@ class TestBase(IPUOpTest):
"weight_decay"
:
0.0
,
}
def
_test_optimizer
(
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
np
.
random
.
seed
(
self
.
SEED
)
with
paddle
.
static
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
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'
]
# Only support ClipGradByGlobalNorm
clip
=
paddle
.
nn
.
ClipGradByGlobalNorm
(
clip_norm
=
1.0
)
if
self
.
attrs
[
'optimizer'
]
==
'sgd'
:
opt
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
1e-1
,
weight_decay
=
weight_decay
,
grad_clip
=
clip
)
elif
self
.
attrs
[
'optimizer'
]
==
'adam'
:
opt
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-1
,
weight_decay
=
weight_decay
,
grad_clip
=
clip
)
elif
self
.
attrs
[
'optimizer'
]
==
'lamb'
:
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-1
,
lamb_weight_decay
=
weight_decay
,
grad_clip
=
clip
)
else
:
raise
ValueError
(
f
"Not supported optimizer
{
self
.
attrs
[
'optimizer'
]
}
for test"
)
opt
.
minimize
(
loss
)
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
run_ipu
:
feed_list
=
[
image
.
name
]
fetch_list
=
[
loss
.
name
]
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
result
=
[]
for
epoch
in
range
(
100
):
loss_res
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
[
loss
])
result
.
append
(
loss_res
)
return
np
.
array
(
result
)
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
100
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
image
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
conv1
=
paddle
.
static
.
nn
.
conv2d
(
image
,
num_filters
=
3
,
filter_size
=
3
,
bias_attr
=
False
)
loss
=
paddle
.
mean
(
conv1
)
self
.
fetch_list
=
[
loss
.
name
]
weight_decay
=
self
.
attrs
[
'weight_decay'
]
# Only support ClipGradByGlobalNorm
clip
=
paddle
.
nn
.
ClipGradByGlobalNorm
(
clip_norm
=
1.0
)
if
self
.
attrs
[
'optimizer'
]
==
'sgd'
:
opt
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
1e-1
,
weight_decay
=
weight_decay
,
grad_clip
=
clip
)
elif
self
.
attrs
[
'optimizer'
]
==
'adam'
:
opt
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-1
,
weight_decay
=
weight_decay
,
grad_clip
=
clip
)
elif
self
.
attrs
[
'optimizer'
]
==
'lamb'
:
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-1
,
lamb_weight_decay
=
weight_decay
,
grad_clip
=
clip
)
else
:
raise
ValueError
(
f
"Not supported optimizer
{
self
.
attrs
[
'optimizer'
]
}
for test"
)
opt
.
minimize
(
loss
)
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
# cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
ipu_loss
=
self
.
_test_optimizer
(
True
).
flatten
()
cpu_loss
=
self
.
_test_optimizer
(
False
).
flatten
()
self
.
assertTrue
(
np
.
allclose
(
ipu_loss
,
cpu_loss
,
atol
=
self
.
atol
)
)
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
(
)
class
TestAdam
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_greater_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
(),
...
...
@@ -28,73 +28,30 @@ class TestGreaterThan(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
.
greater_than
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'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
self
.
op
(
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
]
@
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
=
self
.
op
(
x
,
y
,
**
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
().
astype
(
np
.
int32
)
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_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_fp32
.
values
()]
...
...
python/paddle/fluid/tests/unittests/ipu/test_groupnorm_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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-6
...
...
@@ -56,86 +52,36 @@ class TestBase(IPUOpTest):
"data_layout"
:
'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'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
group_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
else
:
out
=
paddle
.
fluid
.
layers
.
nn
.
group_norm
(
x
,
param_attr
=
True
,
bias_attr
=
True
,
**
self
.
attrs
)
if
self
.
is_training
:
fetch_list
=
[
loss
.
name
]
else
:
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
if
self
.
is_training
:
result
=
[]
for
_
in
range
(
self
.
epoch
):
loss_res
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
[
0
])
return
np
.
array
(
result
)
else
:
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
if
mode
>
ExecutionMode
.
IPU_FP32
and
self
.
is_training
:
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'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
group_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
self
.
fetch_list
=
[
loss
.
name
]
else
:
out
=
paddle
.
fluid
.
layers
.
nn
.
group_norm
(
x
,
param_attr
=
True
,
bias_attr
=
True
,
**
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
):
...
...
@@ -150,7 +96,7 @@ class TestCase1(TestBase):
class
TestTrainCase1
(
TestBase
):
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
1
0
self
.
epoch
=
2
0
@
unittest
.
skipIf
(
IPUOpTest
.
use_ipumodel
(),
"skip for ipumodel"
)
...
...
@@ -170,7 +116,7 @@ class TestTrainCase2(TestBase):
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
1
0
self
.
epoch
=
2
0
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_instancenorm_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
=
1e-6
self
.
rtol
=
1e-5
...
...
@@ -52,86 +48,37 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"epsilon"
:
1e-05
}
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'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
instance_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
else
:
out
=
paddle
.
fluid
.
layers
.
nn
.
instance_norm
(
x
,
param_attr
=
True
,
bias_attr
=
True
,
**
self
.
attrs
)
if
self
.
is_training
:
fetch_list
=
[
loss
.
name
]
else
:
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
if
self
.
is_training
:
result
=
[]
for
_
in
range
(
self
.
epoch
):
loss_res
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
)
return
np
.
array
(
result
)
else
:
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'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
instance_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
adam
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
adam
.
minimize
(
loss
)
self
.
fetch_list
=
[
loss
.
name
]
else
:
out
=
paddle
.
fluid
.
layers
.
nn
.
instance_norm
(
x
,
param_attr
=
True
,
bias_attr
=
True
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
not
self
.
fp16_enabled
:
break
if
mode
>
ExecutionMode
.
IPU_FP32
and
self
.
is_training
:
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
()
class
TestTrainCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_ipu_shard_api.py
已删除
100644 → 0
浏览文件 @
63d4d05a
# 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
paddle
.
enable_static
()
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestIpuShard
(
unittest
.
TestCase
):
def
_test
(
self
):
# build graph
a
=
paddle
.
static
.
data
(
name
=
'data'
,
shape
=
[
None
,
1
],
dtype
=
'int32'
)
b
=
a
+
2
# scale : scale * x + bias, ipu_index : no
with
paddle
.
static
.
ipu_shard_guard
(
index
=
1
):
c
=
b
+
1
# scale, ipu_index : 1
with
paddle
.
static
.
ipu_shard_guard
(
index
=
2
):
d
=
c
*
2
# scale, ipu_index : 2
with
paddle
.
static
.
ipu_shard_guard
(
index
=
3
):
e
=
d
+
3
# scale, ipu_index : 3
with
paddle
.
static
.
ipu_shard_guard
(
index
=
1
):
e
=
e
+
3
# scale, ipu_index : 1
with
paddle
.
static
.
ipu_shard_guard
(
index
=
2
):
e
=
e
+
3
# scale, ipu_index : 2
with
paddle
.
static
.
ipu_shard_guard
(
index
=
1
):
f
=
paddle
.
tensor
.
pow
(
e
,
2.0
)
# pow, ipu_index : 1
with
paddle
.
static
.
ipu_shard_guard
(
index
=
2
):
g
=
f
-
1
# scale, ipu_index : 2
h
=
g
+
1
# scale, ipu_index : no
ipu_index_list
=
[]
main_prog
=
paddle
.
static
.
default_main_program
()
for
op
in
main_prog
.
global_block
().
ops
:
if
op
.
desc
.
has_attr
(
"ipu_index"
):
ipu_index_list
.
append
(
op
.
desc
.
attr
(
"ipu_index"
))
return
ipu_index_list
def
test_ipu_shard
(
self
):
ipu_index_list
=
self
.
_test
()
expected_ipu_index_list
=
[
1
,
2
,
3
,
1
,
2
,
1
,
2
]
self
.
assertTrue
(
np
.
allclose
(
ipu_index_list
,
expected_ipu_index_list
,
atol
=
0
))
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestIpuPipeline
(
unittest
.
TestCase
):
def
_test
(
self
):
# build graph
a
=
paddle
.
static
.
data
(
name
=
'data'
,
shape
=
[
None
,
1
],
dtype
=
'int32'
)
b
=
a
+
2
# scale : scale * x + bias, ipu_stage : no
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
1
):
c
=
b
+
1
# scale, ipu_stage : 1
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
2
):
d
=
c
*
2
# scale, ipu_stage : 2
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
3
):
e
=
d
+
3
# scale, ipu_stage : 3
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
1
):
e
=
e
+
3
# scale, ipu_stage : 1
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
2
):
e
=
e
+
3
# scale, ipu_stage : 2
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
1
):
f
=
paddle
.
tensor
.
pow
(
e
,
2.0
)
# pow, ipu_stage : 1
with
paddle
.
static
.
ipu_shard_guard
(
stage
=
2
):
g
=
f
-
1
# scale, ipu_stage : 2
h
=
g
+
1
# scale, ipu_stage : no
ipu_index_list
=
[]
main_prog
=
paddle
.
static
.
default_main_program
()
for
op
in
main_prog
.
global_block
().
ops
:
if
op
.
desc
.
has_attr
(
"ipu_stage"
):
ipu_index_list
.
append
(
op
.
desc
.
attr
(
"ipu_stage"
))
return
ipu_index_list
def
test_ipu_shard
(
self
):
ipu_index_list
=
self
.
_test
()
expected_ipu_index_list
=
[
1
,
2
,
3
,
1
,
2
,
1
,
2
]
self
.
assertTrue
(
np
.
allclose
(
ipu_index_list
,
expected_ipu_index_list
,
atol
=
0
))
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/ipu/test_layernorm_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
=
1e-6
self
.
rtol
=
1e-5
...
...
@@ -59,89 +55,48 @@ class TestBase(IPUOpTest):
}
self
.
optimizer
=
None
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'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
layer_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
else
:
scale
=
self
.
attrs
[
'scale'
]
bias
=
self
.
attrs
[
'shift'
]
out
=
paddle
.
fluid
.
layers
.
nn
.
layer_norm
(
x
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
fetch_list
=
[
loss
.
name
]
if
self
.
is_training
:
optimizer
=
None
if
self
.
optimizer
==
'sgd'
:
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
1e-2
)
elif
self
.
optimizer
==
'adam'
:
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
elif
self
.
optimizer
==
'lamb'
:
optimizer
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-2
,
lamb_weight_decay
=
0.0
)
if
optimizer
is
not
None
:
optimizer
.
minimize
(
loss
)
if
exec_mode
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
exec_mode
:
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_fp32
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
[
0
])
return
np
.
array
(
result
)
else
:
result
=
exe
.
run
(
program
,
feed
=
self
.
feed_fp32
,
fetch_list
=
fetch_list
)
return
result
[
0
]
def
test_base
(
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
)
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
if
self
.
is_training
:
ch
=
self
.
feed_shape
[
0
][
1
]
conv1
=
paddle
.
static
.
nn
.
conv2d
(
x
,
num_filters
=
ch
,
filter_size
=
3
,
bias_attr
=
False
)
scale
=
paddle
.
ParamAttr
(
trainable
=
True
)
bias
=
paddle
.
ParamAttr
(
trainable
=
True
)
out
=
paddle
.
fluid
.
layers
.
nn
.
layer_norm
(
conv1
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
loss
=
paddle
.
mean
(
out
)
self
.
fetch_list
=
[
loss
.
name
]
else
:
scale
=
self
.
attrs
[
'scale'
]
bias
=
self
.
attrs
[
'shift'
]
out
=
paddle
.
fluid
.
layers
.
nn
.
layer_norm
(
x
,
param_attr
=
scale
,
bias_attr
=
bias
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
if
self
.
is_training
:
optimizer
=
None
if
self
.
optimizer
==
'sgd'
:
optimizer
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
1e-2
)
elif
self
.
optimizer
==
'adam'
:
optimizer
=
paddle
.
optimizer
.
Adam
(
learning_rate
=
1e-2
)
elif
self
.
optimizer
==
'lamb'
:
optimizer
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
1e-2
,
lamb_weight_decay
=
0.0
)
if
optimizer
is
not
None
:
optimizer
.
minimize
(
loss
)
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
(
'raise error'
)
...
...
@@ -188,33 +143,17 @@ class TestTrainCase1(TestBase):
self
.
optimizer
=
'sgd'
def
set_atol
(
self
):
super
().
set_atol
()
self
.
atol
=
1e-6
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
10
class
TestTrainCase2
(
TestBase
):
def
set_atol
(
self
):
self
.
atol
=
5e-4
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"scale"
:
True
,
"shift"
:
True
,
"begin_norm_axis"
:
2
,
"epsilon"
:
1e-05
}
self
.
optimizer
=
'adam'
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
10
self
.
epoch
=
20
class
TestTrainCase3
(
TestBase
):
def
set_atol
(
self
):
super
().
set_atol
()
self
.
atol
=
5e-3
def
set_op_attrs
(
self
):
...
...
@@ -228,7 +167,7 @@ class TestTrainCase3(TestBase):
def
set_training
(
self
):
self
.
is_training
=
True
self
.
epoch
=
1
0
self
.
epoch
=
2
0
# not support `layer_norm(x, param_attr=False, bias_attr=False, **self.attrs)`
...
...
python/paddle/fluid/tests/unittests/ipu/test_log_softmax_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -18,7 +18,7 @@ import numpy as np
import
paddle
import
paddle.nn.functional
as
F
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
(),
...
...
@@ -31,10 +31,6 @@ 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
)}
...
...
@@ -49,59 +45,22 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"axis"
:
-
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'
)
out
=
F
.
log_softmax
(
x
,
**
self
.
attrs
)
fetch_list
=
[
out
.
name
]
if
exec_mode
==
ExecutionMode
.
CPU_FP32
:
place
=
paddle
.
CPUPlace
()
else
:
place
=
paddle
.
IPUPlace
()
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
'float32'
)
out
=
F
.
log_softmax
(
x
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
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
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
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
()
class
TestCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_logical_not_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
(),
...
...
@@ -29,68 +29,32 @@ class TestBase(IPUOpTest):
self
.
set_data_feed
()
self
.
set_feed_attr
()
@
property
def
fp16_enabled
(
self
):
return
True
def
set_data_feed
(
self
):
data
=
np
.
random
.
uniform
(
size
=
[
2
,
20
,
30528
])
self
.
feed
=
{
"in_0"
:
data
.
astype
(
'bool'
)}
self
.
feed_fp32
=
{
"in_0"
:
data
.
astype
(
'bool'
)}
self
.
feed_fp16
=
{
"in_0"
:
data
.
astype
(
'bool'
)}
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
()]
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
=
"bool"
)
out
=
paddle
.
fluid
.
layers
.
logical_not
(
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
result
=
exe
.
run
(
program
,
feed
=
self
.
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
).
astype
(
np
.
int32
)
self
.
check
(
output_dict
,
check_shape
=
True
)
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
()]
@
IPUOpTest
.
static_graph
def
build_model
(
self
):
x
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
0
],
shape
=
self
.
feed_shape
[
0
],
dtype
=
"bool"
)
out
=
paddle
.
fluid
.
layers
.
logical_not
(
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_logical_x_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
(),
...
...
@@ -38,69 +38,38 @@ class TestLogicalAnd(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
=
self
.
feed_dtype
[
0
])
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
self
.
feed_dtype
[
1
])
out
=
self
.
op
(
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
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
])
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
self
.
feed_dtype
[
1
])
out
=
self
.
op
(
x
,
y
,
**
self
.
attrs
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
exec_mode
):
self
.
run_op_test
(
exec_mode
)
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
).
astype
(
np
.
int32
)
self
.
check
(
output_dict
,
check_shape
=
True
)
for
m
in
IPUOpTest
.
ExecutionMode
:
if
not
self
.
skip_mode
(
m
):
self
.
build_model
()
self
.
run_model
(
m
)
self
.
check
()
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
())
self
.
feed_dtype
=
[
'bool'
,
'bool'
]
def
set_data_feed0
(
self
):
x
=
np
.
random
.
choice
([
True
,
False
],
size
=
(
1
,
3
,
5
,
5
))
y
=
np
.
random
.
choice
([
True
,
False
],
size
=
(
1
,
3
,
5
,
5
))
self
.
feed
=
{
self
.
feed
_fp32
=
{
"x"
:
x
.
astype
(
'bool'
),
"y"
:
y
.
astype
(
'bool'
),
}
...
...
python/paddle/fluid/tests/unittests/ipu/test_lookuptable_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
(),
...
...
@@ -30,19 +30,15 @@ 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
.
array
([[[
1
],
[
3
]],
[[
2
],
[
4
]],
[[
4
],
[
127
]]])
self
.
feed_
cpu
=
{
"x"
:
data
.
astype
(
np
.
int64
)}
self
.
feed_
ipu
=
{
"x"
:
data
.
astype
(
np
.
int32
)}
self
.
feed_
fp32
=
{
"x"
:
data
.
astype
(
np
.
int64
)}
self
.
feed_
fp16
=
{
"x"
:
data
.
astype
(
np
.
int32
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_
cpu
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_
cpu
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed_
cpu
.
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
=
{
...
...
@@ -53,76 +49,30 @@ class TestBase(IPUOpTest):
"dtype"
:
'float32'
}
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
=
'int64'
)
out
=
paddle
.
fluid
.
layers
.
embedding
(
x
,
**
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
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_cpu
if
exec_mode
>
ExecutionMode
.
CPU_FP32
:
feed
=
self
.
feed_ipu
if
self
.
is_training
:
result
=
[]
for
_
in
range
(
self
.
epoch
):
loss_res
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
[
0
])
return
np
.
array
(
result
)
else
:
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
=
'int64'
)
out
=
paddle
.
fluid
.
layers
.
embedding
(
x
,
**
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
):
if
self
.
is_ipu_mode
(
exec_mode
):
self
.
feed_fp32
[
'x'
]
=
self
.
feed_fp32
[
'x'
].
astype
(
np
.
int32
)
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
(
not
self
.
fp16_enabled
or
self
.
is_training
):
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
()
class
TestTrainCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_lookuptable_v2_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
(),
...
...
@@ -30,19 +30,15 @@ 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
.
array
([[[
1
],
[
3
]],
[[
2
],
[
4
]],
[[
4
],
[
127
]]])
self
.
feed_
cpu
=
{
"x"
:
x
.
astype
(
np
.
int64
)}
self
.
feed_
ipu
=
{
"x"
:
x
.
astype
(
np
.
int32
)}
self
.
feed_
fp32
=
{
"x"
:
x
.
astype
(
np
.
int64
)}
self
.
feed_
fp16
=
{
"x"
:
x
.
astype
(
np
.
int32
)}
def
set_feed_attr
(
self
):
self
.
feed_shape
=
[
x
.
shape
for
x
in
self
.
feed_
cpu
.
values
()]
self
.
feed_list
=
list
(
self
.
feed_
cpu
.
keys
())
self
.
feed_dtype
=
[
x
.
dtype
for
x
in
self
.
feed_
cpu
.
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
=
{
...
...
@@ -53,76 +49,31 @@ class TestBase(IPUOpTest):
"weight_attr"
:
None
}
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
=
'int64'
)
embedding
=
paddle
.
nn
.
Embedding
(
**
self
.
attrs
)
out
=
embedding
(
x
)
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
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_cpu
if
exec_mode
>
ExecutionMode
.
CPU_FP32
:
feed
=
self
.
feed_ipu
if
self
.
is_training
:
result
=
[]
for
_
in
range
(
self
.
epoch
):
loss_res
=
exe
.
run
(
program
,
feed
=
feed
,
fetch_list
=
fetch_list
)
result
.
append
(
loss_res
[
0
])
return
np
.
array
(
result
)
else
:
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
=
'int64'
)
embedding
=
paddle
.
nn
.
Embedding
(
**
self
.
attrs
)
out
=
embedding
(
x
)
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
):
if
self
.
is_ipu_mode
(
exec_mode
):
self
.
feed_fp32
[
'x'
]
=
self
.
feed_fp32
[
'x'
].
astype
(
np
.
int32
)
self
.
run_op_test
(
exec_mode
)
def
test
(
self
):
output_dict
=
{}
for
mode
in
ExecutionMode
:
if
mode
>
ExecutionMode
.
IPU_FP32
and
(
not
self
.
fp16_enabled
or
self
.
is_training
):
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
()
class
TestTrainCase1
(
TestBase
):
...
...
python/paddle/fluid/tests/unittests/ipu/test_lr_sheduler_ipu.py
浏览文件 @
063a3509
...
...
@@ -12,89 +12,75 @@
# 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
sys
import
paddle
import
paddle.fluid
as
fluid
import
paddle.static
from
paddle.optimizer.lr
import
LRScheduler
paddle
.
enable_static
()
SEED
=
2021
from
paddle.fluid.tests.unittests.ipu.op_test_ipu
import
IPUOpTest
class
LR_New
(
LRScheduler
):
def
__init__
(
self
,
learning_rate
=
1
.0
,
last_epoch
=-
1
,
verbose
=
False
):
def
__init__
(
self
,
learning_rate
=
1
e-5
,
last_epoch
=-
1
,
verbose
=
False
):
super
(
LR_New
,
self
).
__init__
(
learning_rate
,
last_epoch
,
verbose
)
def
get_lr
(
self
):
self
.
base_lr
=
self
.
base_lr
+
1
self
.
base_lr
=
self
.
base_lr
+
1
e-4
self
.
last_epoch
=
self
.
last_epoch
+
1
return
self
.
base_lr
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestConvNet
(
unittest
.
TestCase
):
def
_test
(
self
,
run_ipu
=
True
):
scope
=
fluid
.
core
.
Scope
()
main_prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
main_prog
.
random_seed
=
SEED
startup_prog
.
random_seed
=
SEED
np
.
random
.
seed
(
SEED
)
np_image
=
np
.
random
.
rand
(
1
,
3
,
10
,
10
).
astype
(
np
.
float32
)
with
fluid
.
scope_guard
(
scope
):
with
paddle
.
static
.
program_guard
(
main_prog
,
startup_prog
):
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
)
sgd
=
paddle
.
optimizer
.
SGD
(
learning_rate
=
LR_New
())
sgd
.
minimize
(
loss
)
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
if
run_ipu
:
feed_list
=
[
image
.
name
]
fetch_list
=
[
loss
.
name
]
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
True
)
program
=
paddle
.
static
.
IpuCompiledProgram
(
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
fetch_list
)
else
:
program
=
main_prog
result
=
[]
for
epoch
in
range
(
100
):
if
hasattr
(
program
,
"lr_sheduler"
):
program
.
lr_sheduler
.
step
()
loss_res
=
exe
.
run
(
program
,
feed
=
{
image
.
name
:
np_image
},
fetch_list
=
[
loss
])
result
.
append
(
loss_res
)
return
np
.
array
(
result
)
class
TestConvNet
(
IPUOpTest
):
@
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
)
opt
=
paddle
.
optimizer
.
Lamb
(
learning_rate
=
LR_New
())
opt
.
minimize
(
loss
)
self
.
feed_list
=
[
image
.
name
]
self
.
fetch_list
=
[
loss
.
name
]
def
run_model
(
self
,
run_ipu
=
True
):
self
.
build_model
()
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
self
.
startup_prog
)
if
run_ipu
:
ipu_strategy
=
paddle
.
static
.
IpuStrategy
()
ipu_strategy
.
set_graph_config
(
is_training
=
True
)
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
(
100
):
if
hasattr
(
program
,
"lr_sheduler"
):
program
.
lr_sheduler
.
step
()
loss_res
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
self
.
fetch_list
)
result
.
append
(
loss_res
)
return
np
.
array
(
result
)
def
test_training
(
self
):
data
=
np
.
random
.
rand
(
1
,
3
,
10
,
10
).
astype
(
np
.
float32
)
self
.
feed
=
{
'image'
:
data
}
# cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
ipu_loss
=
self
.
_test
(
True
).
flatten
()
cpu_loss
=
self
.
_test
(
False
).
flatten
()
ipu_loss
=
self
.
run_model
(
True
).
flatten
()
cpu_loss
=
self
.
run_model
(
False
).
flatten
()
self
.
assertTrue
(
np
.
allclose
(
ipu_loss
,
cpu_loss
,
atol
=
1e-
4
))
self
.
assertTrue
(
np
.
allclose
(
ipu_loss
,
cpu_loss
,
atol
=
1e-
10
))
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/tests/unittests/ipu/test_matmul_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
=
[
20
,
30
])
y
=
np
.
random
.
uniform
(
size
=
[
30
,
20
])
...
...
@@ -52,63 +48,25 @@ class TestBase(IPUOpTest):
"alpha"
:
1.0
,
}
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'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
fluid
.
layers
.
matmul
(
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
.
matmul
(
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_matmul_serilize_ipu.py
浏览文件 @
063a3509
...
...
@@ -26,7 +26,7 @@ def set_serialize_factor(serialize_factor):
op
.
_set_attr
(
'serialize_factor'
,
serialize_factor
)
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
()
or
IPUOpTest
.
use_ipumodel
()
,
@
unittest
.
skipIf
(
not
paddle
.
is_compiled_with_ipu
(),
"core is not compiled with IPU"
)
class
TestBase
(
IPUOpTest
):
def
setUp
(
self
):
...
...
@@ -38,8 +38,8 @@ class TestBase(IPUOpTest):
def
set_data_feed
(
self
):
self
.
feed
=
{
"x"
:
np
.
random
.
uniform
(
size
=
[
2048
,
307
2
]).
astype
(
'float32'
),
"y"
:
np
.
random
.
uniform
(
size
=
[
3
072
,
2048
]).
astype
(
'float32'
),
"x"
:
np
.
random
.
uniform
(
size
=
[
16
,
3
2
]).
astype
(
'float32'
),
"y"
:
np
.
random
.
uniform
(
size
=
[
3
2
,
16
]).
astype
(
'float32'
),
}
def
set_feed_attr
(
self
):
...
...
@@ -50,58 +50,47 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"transpose_x"
:
False
,
"transpose_y"
:
False
}
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
])
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
self
.
feed_dtype
[
1
])
# decrator maybe the best choice, but need to modify api
out
=
paddle
.
matmul
(
x
,
y
,
**
self
.
attrs
)
set_serialize_factor
(
4
)
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
]
@
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
])
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
self
.
feed_dtype
[
1
])
# decrator maybe the best choice, but need to modify api
out
=
paddle
.
matmul
(
x
,
y
,
**
self
.
attrs
)
set_serialize_factor
(
4
)
self
.
fetch_list
=
[
out
.
name
]
def
run_model
(
self
,
run_ipu
):
self
.
build_model
()
if
run_ipu
:
place
=
paddle
.
IPUPlace
()
else
:
place
=
paddle
.
CPUPlace
()
exe
=
paddle
.
static
.
Executor
(
place
)
exe
.
run
(
self
.
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
(
self
.
main_prog
,
ipu_strategy
=
ipu_strategy
).
compile
(
feed_list
,
self
.
fetch_list
)
else
:
program
=
self
.
main_prog
result
=
exe
.
run
(
program
,
feed
=
self
.
feed
,
fetch_list
=
self
.
fetch_list
)
return
result
[
0
]
def
test_base
(
self
):
res0
=
self
.
_test_base
(
False
)
res1
=
self
.
_test_base
(
True
)
res0
=
self
.
run_model
(
False
)
res1
=
self
.
run_model
(
True
)
self
.
assertTrue
(
np
.
allclose
(
res0
.
flatten
(),
res1
.
flatten
(),
atol
=
self
.
atol
))
self
.
assertTrue
(
res0
.
shape
==
res1
.
shape
)
...
...
python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py
浏览文件 @
063a3509
...
...
@@ -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
,
3
])
y
=
np
.
random
.
uniform
(
size
=
[
3
,
2
])
...
...
@@ -48,63 +44,24 @@ class TestBase(IPUOpTest):
def
set_op_attrs
(
self
):
self
.
attrs
=
{
"transpose_x"
:
False
,
"transpose_y"
:
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'
)
y
=
paddle
.
static
.
data
(
name
=
self
.
feed_list
[
1
],
shape
=
self
.
feed_shape
[
1
],
dtype
=
'float32'
)
out
=
paddle
.
matmul
(
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
.
matmul
(
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
):
...
...
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