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wenyuanbo
tic
Commits
6c62dac3
Commit
6c62dac3
authored
Jul 02, 2018
by
Tianqi Chen
Browse files
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[TVM] Upgrade TVM Support
parent
3ae9e155
Show whitespace changes
Inline
Side-by-side
Showing
5 changed files
with
50 additions
and
108 deletions
+50
-108
vta/docs/how_to/install.md
+1
-25
vta/examples/resnet18/pynq/imagenet_predict.py
+8
-6
vta/python/vta/graph.py
+14
-57
vta/python/vta/top/vta_conv2d.py
+26
-19
vta/tests/python/integration/test_benchmark_topi_conv2d.py
+1
-1
No files found.
vta/docs/how_to/install.md
View file @
6c62dac3
...
@@ -59,31 +59,7 @@ In the 'config.mk' file, make sure that:
...
@@ -59,31 +59,7 @@ In the 'config.mk' file, make sure that:
For the
*Python Package Installation*
, we recommend updating your
`~/.bashrc`
file to extend your
`PYTHONPATH`
with the TVM Python libraries.
For the
*Python Package Installation*
, we recommend updating your
`~/.bashrc`
file to extend your
`PYTHONPATH`
with the TVM Python libraries.
```
bash
```
bash
export
PYTHONPATH
=
<tvm root>/python:<tvm root>/topi/python:
${
PYTHONPATH
}
export
PYTHONPATH
=
<tvm root>/python:<tvm root>/topi/python:<tvm root>/nnvm/python:
${
PYTHONPATH
}
```
#### NNVM Installation
Clone the NNVM repository from
`tqchen`
in the directory of your choosing:
```
bash
git clone git@github.com:tqchen/nnvm.git
--recursive
```
To run this example, we rely on a special branch of NNVM
`qt`
:
```
bash
cd
<nnvm root>
git checkout qt
```
Launch the compilation, this takes about a minute on two threads.
```
bash
cd
<nnvm root>
make
-j2
```
Finally update your
`~/.bashrc`
file to include the NNVM python libraries in your
`PYTHONPATH`
:
```
bash
export
PYTHONPATH
=
<nnvm root>/python:
${
PYTHONPATH
}
```
```
#### MxNet Installation
#### MxNet Installation
...
...
vta/examples/resnet18/pynq/imagenet_predict.py
View file @
6c62dac3
...
@@ -26,8 +26,8 @@ data_dir = "_data/"
...
@@ -26,8 +26,8 @@ data_dir = "_data/"
url
=
"https://homes.cs.washington.edu/~moreau/media/vta/"
url
=
"https://homes.cs.washington.edu/~moreau/media/vta/"
TEST_FILE
=
'cat.jpg'
TEST_FILE
=
'cat.jpg'
CATEG_FILE
=
'synset.txt'
CATEG_FILE
=
'synset.txt'
RESNET_GRAPH_FILE
=
'
quantize_graph
.json'
RESNET_GRAPH_FILE
=
'
resnet18_qt8
.json'
RESNET_PARAMS_FILE
=
'
quantize
_params.pkl'
RESNET_PARAMS_FILE
=
'
resnet18_qt8
_params.pkl'
# Create data dir
# Create data dir
if
not
os
.
path
.
exists
(
data_dir
):
if
not
os
.
path
.
exists
(
data_dir
):
os
.
makedirs
(
data_dir
)
os
.
makedirs
(
data_dir
)
...
@@ -70,7 +70,7 @@ def mark_nop(graph, conv_layer=-1, skip_conv_layer=()):
...
@@ -70,7 +70,7 @@ def mark_nop(graph, conv_layer=-1, skip_conv_layer=()):
attrs
=
node
[
"attrs"
]
attrs
=
node
[
"attrs"
]
node_name
=
node
[
"name"
]
node_name
=
node
[
"name"
]
func_name
=
attrs
[
"func_name"
]
func_name
=
attrs
[
"func_name"
]
if
func_name
.
find
(
"
quantized_
conv2d"
)
!=
-
1
:
if
func_name
.
find
(
"conv2d"
)
!=
-
1
:
if
conv_layer
>=
0
:
if
conv_layer
>=
0
:
if
counter
!=
conv_layer
:
if
counter
!=
conv_layer
:
attrs
[
"func_name"
]
=
"__nop"
attrs
[
"func_name"
]
=
"__nop"
...
@@ -109,9 +109,9 @@ graph_attr.set_dtype_inputs(sym, dtype_dict)
...
@@ -109,9 +109,9 @@ graph_attr.set_dtype_inputs(sym, dtype_dict)
graph
=
graph
.
apply
(
"InferShape"
)
.
apply
(
"InferType"
)
graph
=
graph
.
apply
(
"InferShape"
)
.
apply
(
"InferType"
)
dtype
=
"float32"
dtype
=
"float32"
sym
=
vta
.
graph
.
remove_stochastic
(
sym
)
sym
=
vta
.
graph
.
clean_cast
(
sym
)
sym
=
vta
.
graph
.
clean_cast
(
sym
)
sym
=
vta
.
graph
.
clean_conv_fuse
(
sym
)
sym
=
vta
.
graph
.
clean_conv_fuse
(
sym
)
if
target
.
device_name
==
"vta"
:
if
target
.
device_name
==
"vta"
:
sym
=
vta
.
graph
.
pack
(
sym
,
shape_dict
,
bfactor
,
cfactor
)
sym
=
vta
.
graph
.
pack
(
sym
,
shape_dict
,
bfactor
,
cfactor
)
...
@@ -166,8 +166,10 @@ def run_e2e(graph):
...
@@ -166,8 +166,10 @@ def run_e2e(graph):
# get outputs
# get outputs
tvm_output
=
m
.
get_output
(
tvm_output
=
m
.
get_output
(
0
,
tvm
.
nd
.
empty
((
1000
,),
dtype
,
remote
.
cpu
(
0
)))
0
,
tvm
.
nd
.
empty
((
1000
,),
dtype
,
remote
.
cpu
(
0
)))
top1
=
np
.
argmax
(
tvm_output
.
asnumpy
())
print
(
'TVM prediction top-1:'
,
top1
,
synset
[
top1
])
top
=
list
(
reversed
(
np
.
argsort
(
tvm_output
.
asnumpy
())))
for
i
in
range
(
5
):
print
(
'TVM prediction top-
%
d:
%
s'
%
(
i
,
synset
[
top
[
i
]]))
print
(
"t-cost=
%
g"
%
tcost
.
mean
)
print
(
"t-cost=
%
g"
%
tcost
.
mean
)
...
...
vta/python/vta/graph.py
View file @
6c62dac3
...
@@ -71,48 +71,6 @@ def _get_shape(sym, shape_dict):
...
@@ -71,48 +71,6 @@ def _get_shape(sym, shape_dict):
return
graph_util
.
infer_shape
(
return
graph_util
.
infer_shape
(
nnvm
.
graph
.
create
(
sym
),
**
shape_dict
)[
1
][
0
]
nnvm
.
graph
.
create
(
sym
),
**
shape_dict
)[
1
][
0
]
def
remove_stochastic
(
graph
):
"""
Replace stochastic rounding and shift with determinstic version.
Parameters
----------
graph : Graph
The input graph
Returns
-------
replaced_graph : Graph
The final replaced graph.
"""
gidx
=
graph
.
index
node_map
=
{}
for
nid
,
node
in
enumerate
(
gidx
.
nodes
):
children
=
[
node_map
[
e
[
0
]]
for
e
in
node
[
"inputs"
]]
attrs
=
node
.
get
(
"attrs"
,
{})
node_name
=
node
[
"name"
]
op_name
=
node
[
"op"
]
get_clone
=
lambda
c
,
o_n
,
n_n
,
a
:
getattr
(
nnvm
.
symbol
,
o_n
)(
*
c
,
name
=
n_n
,
**
a
)
if
op_name
==
"null"
:
new_node
=
nnvm
.
symbol
.
Variable
(
node_name
)
elif
op_name
==
"stochastic_round"
:
new_node
=
children
[
0
]
elif
op_name
==
"noise_lshift"
:
new_node
=
nnvm
.
symbol
.
left_shift
(
children
[
0
],
**
attrs
)
else
:
new_node
=
get_clone
(
children
,
op_name
,
node_name
,
attrs
)
node_map
[
nid
]
=
new_node
assert
len
(
graph
.
index
.
output_entries
)
==
1
ret
=
node_map
[
graph
.
index
.
output_entries
[
0
][
0
]]
ret
=
nnvm
.
graph
.
create
(
ret
)
return
ret
def
clean_conv_fuse
(
graph
):
def
clean_conv_fuse
(
graph
):
"""Cleanup the convolution's later fuse stages
"""Cleanup the convolution's later fuse stages
...
@@ -131,8 +89,8 @@ def clean_conv_fuse(graph):
...
@@ -131,8 +89,8 @@ def clean_conv_fuse(graph):
if
flag
:
if
flag
:
node
=
nnvm
.
symbol
.
clip
(
node
,
a_max
=
127
,
a_min
=-
127
)
node
=
nnvm
.
symbol
.
clip
(
node
,
a_max
=
127
,
a_min
=-
127
)
node
=
nnvm
.
symbol
.
cast
(
node
,
dtype
=
"int8"
)
node
=
nnvm
.
symbol
.
cast
(
node
,
dtype
=
"int8"
)
# Use
identit
y as a hint to block conv2d schedules
# Use
cop
y as a hint to block conv2d schedules
node
=
nnvm
.
symbol
.
identit
y
(
node
)
node
=
nnvm
.
symbol
.
cop
y
(
node
)
flag
=
False
flag
=
False
return
node
,
flag
return
node
,
flag
...
@@ -166,13 +124,13 @@ def clean_conv_fuse(graph):
...
@@ -166,13 +124,13 @@ def clean_conv_fuse(graph):
new_entry
=
(
new_entry
=
(
get_clone
([
children
[
0
][
0
]],
op_name
,
node_name
,
attrs
),
get_clone
([
children
[
0
][
0
]],
op_name
,
node_name
,
attrs
),
False
)
False
)
elif
op_name
==
"
quantized_conv2d
"
:
elif
op_name
==
"
conv2d"
and
attrs
[
"out_dtype"
]
==
"int32
"
:
data
,
weight
=
children
data
,
weight
=
children
data
=
_clean_entry
(
data
)
data
=
_clean_entry
(
data
)
new_node
=
nnvm
.
sym
.
quantized_
conv2d
(
new_node
=
nnvm
.
sym
.
conv2d
(
data
[
0
],
weight
[
0
],
name
=
node_name
,
**
attrs
)
data
[
0
],
weight
[
0
],
name
=
node_name
,
**
attrs
)
new_entry
=
(
new_node
,
True
)
new_entry
=
(
new_node
,
True
)
elif
op_name
in
(
"
left_shift"
,
"right_shift
"
,
"relu"
):
elif
op_name
in
(
"
__lshift_scalar__"
,
"__rshift_scalar__
"
,
"relu"
):
new_entry
=
(
new_entry
=
(
get_clone
([
children
[
0
][
0
]],
op_name
,
node_name
,
attrs
),
get_clone
([
children
[
0
][
0
]],
op_name
,
node_name
,
attrs
),
children
[
0
][
1
])
children
[
0
][
1
])
...
@@ -199,7 +157,6 @@ def clean_conv_fuse(graph):
...
@@ -199,7 +157,6 @@ def clean_conv_fuse(graph):
ret
=
nnvm
.
graph
.
create
(
ret
)
ret
=
nnvm
.
graph
.
create
(
ret
)
return
ret
return
ret
def
clean_cast
(
graph
):
def
clean_cast
(
graph
):
"""
"""
Move the casts to early part of graph,
Move the casts to early part of graph,
...
@@ -232,11 +189,11 @@ def clean_cast(graph):
...
@@ -232,11 +189,11 @@ def clean_cast(graph):
elif
op_name
==
"cast"
:
elif
op_name
==
"cast"
:
dtype
=
attrs
[
"dtype"
]
dtype
=
attrs
[
"dtype"
]
new_node
,
_
=
_clean_cast
(
children
[
0
],
dtype
)
new_node
,
_
=
_clean_cast
(
children
[
0
],
dtype
)
elif
op_name
==
"
quantized_conv2d
"
:
elif
op_name
==
"
conv2d"
and
attrs
[
"out_dtype"
]
==
"int32
"
:
data
,
weight
=
children
data
,
weight
=
children
data
,
_
=
_clean_cast
(
data
,
"int8"
)
data
,
_
=
_clean_cast
(
data
,
"int8"
)
weight
,
_
=
_clean_cast
(
weight
,
"int8"
)
weight
,
_
=
_clean_cast
(
weight
,
"int8"
)
new_node
=
nnvm
.
sym
.
quantized_
conv2d
(
new_node
=
nnvm
.
sym
.
conv2d
(
data
,
weight
,
name
=
node_name
,
**
attrs
)
data
,
weight
,
name
=
node_name
,
**
attrs
)
elif
op_name
==
"elemwise_add"
:
elif
op_name
==
"elemwise_add"
:
lhs
,
rhs
=
children
lhs
,
rhs
=
children
...
@@ -314,21 +271,21 @@ def pack(graph, shape_dict, bfactor, cfactor, start_name=None):
...
@@ -314,21 +271,21 @@ def pack(graph, shape_dict, bfactor, cfactor, start_name=None):
*
children
,
name
=
node_name
,
**
attrs
)
*
children
,
name
=
node_name
,
**
attrs
)
else
:
else
:
new_node
=
get_clone
(
children
,
op_name
,
node_name
,
attrs
)
new_node
=
get_clone
(
children
,
op_name
,
node_name
,
attrs
)
elif
op_name
==
"
quantized_conv2d
"
:
elif
op_name
==
"
conv2d"
and
attrs
[
"out_dtype"
]
==
"int32
"
:
if
start_pack
:
if
start_pack
:
attrs
[
"
pack_batch"
]
=
str
(
b
factor
)
attrs
[
"
layout"
]
=
"NCHW
%
dn
%
dc"
%
(
bfactor
,
c
factor
)
attrs
[
"
pack_channel"
]
=
str
(
cfactor
)
attrs
[
"
kernel_layout"
]
=
"OIHW
%
do
%
di"
%
(
cfactor
,
cfactor
)
data
,
weight
=
children
data
,
weight
=
children
weight
=
_pack_weight
(
weight
,
ishape
[
1
],
cfactor
)
weight
=
_pack_weight
(
weight
,
ishape
[
1
],
cfactor
)
new_node
=
nnvm
.
sym
.
quantized_
conv2d
(
new_node
=
nnvm
.
sym
.
conv2d
(
data
,
weight
,
name
=
node_name
,
**
attrs
)
data
,
weight
,
name
=
node_name
,
**
attrs
)
elif
counter
==
1
:
elif
counter
==
1
:
attrs
[
"
pack_batch"
]
=
str
(
b
factor
)
attrs
[
"
layout"
]
=
"NCHW
%
dn
%
dc"
%
(
bfactor
,
c
factor
)
attrs
[
"
pack_channel"
]
=
str
(
cfactor
)
attrs
[
"
kernel_layout"
]
=
"OIHW
%
do
%
di"
%
(
cfactor
,
cfactor
)
data
,
weight
=
children
data
,
weight
=
children
data
=
_pack_batch_channel
(
data
,
ishape
[
0
],
bfactor
,
cfactor
)
data
=
_pack_batch_channel
(
data
,
ishape
[
0
],
bfactor
,
cfactor
)
weight
=
_pack_weight
(
weight
,
ishape
[
1
],
cfactor
)
weight
=
_pack_weight
(
weight
,
ishape
[
1
],
cfactor
)
new_node
=
nnvm
.
sym
.
quantized_
conv2d
(
new_node
=
nnvm
.
sym
.
conv2d
(
data
,
weight
,
name
=
node_name
,
**
attrs
)
data
,
weight
,
name
=
node_name
,
**
attrs
)
new_node
=
_unpack_batch_channel
(
new_node
,
oshape
)
new_node
=
_unpack_batch_channel
(
new_node
,
oshape
)
counter
=
counter
+
1
counter
=
counter
+
1
...
...
vta/python/vta/top/vta_conv2d.py
View file @
6c62dac3
...
@@ -215,7 +215,7 @@ def _lower(sch, inputs, func_name, graph):
...
@@ -215,7 +215,7 @@ def _lower(sch, inputs, func_name, graph):
f
,
(
tvm
.
container
.
Array
,
tuple
,
list
))
else
[
f
]
f
,
(
tvm
.
container
.
Array
,
tuple
,
list
))
else
[
f
]
@reg.register_compute
(
"clip"
,
level
=
1
1
)
@reg.register_compute
(
"clip"
,
level
=
1
5
)
def
compute_clip
(
attrs
,
inputs
,
_
):
def
compute_clip
(
attrs
,
inputs
,
_
):
""" Clip operator.
""" Clip operator.
"""
"""
...
@@ -231,11 +231,24 @@ def compute_clip(attrs, inputs, _):
...
@@ -231,11 +231,24 @@ def compute_clip(attrs, inputs, _):
x
.
shape
,
lambda
*
i
:
tvm
.
max
(
x
(
*
i
),
const_min
),
name
=
"clipB"
)
x
.
shape
,
lambda
*
i
:
tvm
.
max
(
x
(
*
i
),
const_min
),
name
=
"clipB"
)
return
x
return
x
# override to force partition at copy
reg
.
register_pattern
(
"copy"
,
OpPattern
.
INJECTIVE
,
level
=
15
)
reg
.
register_pattern
(
"identity"
,
OpPattern
.
INJECTIVE
,
level
=
11
)
def
is_packed_layout
(
layout
):
"""Check if layout is packed layout"""
if
layout
==
"NCHW"
:
return
False
assert
"n"
in
layout
assert
"c"
in
layout
return
True
@reg.register_compute
(
"quantized_conv2d"
,
level
=
11
)
@reg.register_alter_op_layout
(
"conv2d"
,
level
=
15
)
def
compute_quantized_conv2d
(
attrs
,
inputs
,
out
):
def
alter_conv2d_layout
(
*
_
):
return
None
@reg.register_compute
(
"conv2d"
,
level
=
15
)
def
compute_conv2d
(
attrs
,
inputs
,
out
):
""" 2D convolution algorithm.
""" 2D convolution algorithm.
"""
"""
padding
=
attrs
.
get_int_tuple
(
"padding"
)
padding
=
attrs
.
get_int_tuple
(
"padding"
)
...
@@ -244,36 +257,30 @@ def compute_quantized_conv2d(attrs, inputs, out):
...
@@ -244,36 +257,30 @@ def compute_quantized_conv2d(attrs, inputs, out):
groups
=
attrs
.
get_int
(
"groups"
)
groups
=
attrs
.
get_int
(
"groups"
)
channels
=
attrs
.
get_int
(
"channels"
)
channels
=
attrs
.
get_int
(
"channels"
)
layout
=
attrs
[
"layout"
]
layout
=
attrs
[
"layout"
]
out_dtype
=
attrs
[
'out_type'
]
out_dtype
=
attrs
[
'out_dtype'
]
cmp_dtype
=
'int32'
# compute data type
assert
layout
==
"NCHW"
,
"only support nchw for now"
assert
dilation
==
(
1
,
1
),
"not support dilate now"
assert
dilation
==
(
1
,
1
),
"not support dilate now"
assert
attrs
.
get_bool
(
"use_bias"
)
is
False
assert
attrs
.
get_bool
(
"use_bias"
)
is
False
pack_channel
=
attrs
.
get_int
(
"pack_channel"
)
if
is_packed_layout
(
layout
):
if
pack_channel
!=
0
:
assert
groups
==
1
assert
groups
==
1
return
packed_conv2d
(
inputs
[
0
],
inputs
[
1
],
return
packed_conv2d
(
inputs
[
0
],
inputs
[
1
],
padding
,
strides
)
padding
,
strides
,
out_dtype
=
out_dtype
)
if
groups
==
1
:
if
groups
==
1
:
out
=
topi
.
nn
.
conv2d
(
inputs
[
0
],
inputs
[
1
],
strides
,
padding
,
out_dtype
=
cmp
_dtype
)
out
=
topi
.
nn
.
conv2d
(
inputs
[
0
],
inputs
[
1
],
strides
,
padding
,
out_dtype
=
out
_dtype
)
elif
groups
==
get_const_int
(
inputs
[
0
]
.
shape
[
1
])
and
groups
==
channels
:
elif
groups
==
get_const_int
(
inputs
[
0
]
.
shape
[
1
])
and
groups
==
channels
:
out
=
topi
.
nn
.
depthwise_conv2d_nchw
(
out
=
topi
.
nn
.
depthwise_conv2d_nchw
(
inputs
[
0
],
inputs
[
1
],
strides
,
padding
,
out_dtype
=
cmp
_dtype
)
inputs
[
0
],
inputs
[
1
],
strides
,
padding
,
out_dtype
=
out
_dtype
)
else
:
else
:
raise
ValueError
(
"not support arbitrary group number for now"
)
raise
ValueError
(
"not support arbitrary group number for now"
)
assert
out_dtype
==
cmp_dtype
return
out
return
out
@reg.register_schedule
(
"
quantized_conv2d"
,
level
=
11
)
@reg.register_schedule
(
"
conv2d"
,
level
=
15
)
def
schedule_quantized_conv2d
(
attrs
,
outs
,
target
):
def
schedule_quantized_conv2d
(
attrs
,
outs
,
target
):
""" 2D convolution schedule.
""" 2D convolution schedule.
"""
"""
channels
=
attrs
.
get_int
(
"channels"
)
layout
=
attrs
[
"layout"
]
pack_channel
=
attrs
.
get_int
(
"pack_channel"
)
if
channels
!=
0
and
pack_channel
:
if
is_packed_layout
(
layout
)
:
target
=
tvm
.
target
.
create
(
target
)
target
=
tvm
.
target
.
create
(
target
)
if
target
.
device_name
==
"vta"
:
if
target
.
device_name
==
"vta"
:
return
schedule_packed_conv2d
(
outs
)
return
schedule_packed_conv2d
(
outs
)
...
...
vta/tests/python/integration/test_benchmark_topi_conv2d.py
View file @
6c62dac3
...
@@ -38,7 +38,7 @@ def test_vta_conv2d():
...
@@ -38,7 +38,7 @@ def test_vta_conv2d():
res_conv
=
vta
.
top
.
packed_conv2d
(
res_conv
=
vta
.
top
.
packed_conv2d
(
data
,
kernel
,
padding
=
(
wl
.
hpad
,
wl
.
wpad
),
strides
=
(
wl
.
hstride
,
wl
.
wstride
))
data
,
kernel
,
padding
=
(
wl
.
hpad
,
wl
.
wpad
),
strides
=
(
wl
.
hstride
,
wl
.
wstride
))
res
=
topi
.
right_shift
(
res_conv
,
8
)
res
=
topi
.
right_shift
(
res_conv
,
8
)
res
=
topi
.
broadcast_
add
(
res
,
bias
)
res
=
topi
.
add
(
res
,
bias
)
res
=
my_clip
(
res
,
0
,
127
)
res
=
my_clip
(
res
,
0
,
127
)
res
=
topi
.
cast
(
res
,
"int8"
)
res
=
topi
.
cast
(
res
,
"int8"
)
...
...
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