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This is an archived project. Repository and other project resources are read-only.
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pycodegen
pystencils_autodiff
Commits
1c955a6a
Commit
1c955a6a
authored
5 years ago
by
Stephan Seitz
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Plain Diff
Regard constant_fields in ops hash in _torch_native
parent
943505f7
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Pipeline
#21053
failed
5 years ago
Stage: test
Changes
1
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1
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1 changed file
src/pystencils_autodiff/backends/_torch_native.py
+17
-6
17 additions, 6 deletions
src/pystencils_autodiff/backends/_torch_native.py
with
17 additions
and
6 deletions
src/pystencils_autodiff/backends/_torch_native.py
+
17
−
6
View file @
1c955a6a
...
...
@@ -19,15 +19,16 @@ def create_autograd_function(autodiff_obj, use_cuda):
if
use_cuda
:
forward_ast
=
autodiff_obj
.
forward_ast_gpu
backward_ast
=
autodiff_obj
.
backward_ast_gpu
backward_ast
=
autodiff_obj
.
backward_ast_gpu
if
autodiff_obj
.
backward_output_fields
else
None
else
:
forward_ast
=
autodiff_obj
.
forward_ast_cpu
backward_ast
=
autodiff_obj
.
backward_ast_cpu
backward_ast
=
autodiff_obj
.
backward_ast_cpu
if
autodiff_obj
.
backward_output_fields
else
None
op_name
=
f
'
{
autodiff_obj
.
op_name
}
_
{
_hash
((
str
(
pystencils
.
show_code
(
forward_ast
))
+
str
(
autodiff_obj
)).
encode
()).
hexdigest
()
}
'
# noqa
op_name
=
f
'
{
autodiff_obj
.
op_name
}
_
{
_hash
((
str
(
pystencils
.
show_code
(
forward_ast
))
+
str
(
autodiff_obj
)
+
str
(
autodiff_obj
.
constant_fields
)
).
encode
()).
hexdigest
()
}
'
# noqa
forward_ast
.
function_name
=
f
'
{
op_name
}
_
{
forward_ast
.
function_name
}
'
backward_ast
.
function_name
=
f
'
{
op_name
}
_
{
backward_ast
.
function_name
}
'
module
=
TorchModule
(
op_name
,
[
forward_ast
,
backward_ast
])
if
backward_ast
:
backward_ast
.
function_name
=
f
'
{
op_name
}
_
{
backward_ast
.
function_name
}
'
module
=
TorchModule
(
op_name
,
[
forward_ast
,
backward_ast
]
if
backward_ast
else
[
forward_ast
])
compiled_op
=
module
.
compile
()
# print(TorchModule(op_name, [forward_ast, backward_ast]))
...
...
@@ -103,16 +104,26 @@ def create_autograd_function(autodiff_obj, use_cuda):
return
tuple
(
backward_output_tensors
.
values
())
def
call
(
self
,
**
kwargs
):
rtn
=
self
.
apply
(
*
[
kwargs
[
p
.
symbol
.
name
]
for
p
in
self
.
forward_parameters
])
if
len
(
rtn
)
==
1
:
rtn
=
rtn
[
0
]
return
rtn
cls
=
type
(
op_name
,
(
torch
.
autograd
.
Function
,),
{})
cls
.
class_kwargs
=
class_kwargs
cls
.
forward
=
forward
cls
.
backward
=
backward
cls
.
kernel
=
forward
cls
.
ast
=
module
cls
.
parameters
=
forward_ast
.
get_parameters
()
cls
.
parameters
=
[
f
for
f
in
module
.
kernel_wrappers
if
f
.
function_name
==
"
call_
"
+
forward_ast
.
function_name
][
0
].
get_parameters
()
cls
.
forward_parameters
=
[
p
for
p
in
cls
.
parameters
if
p
.
symbol
.
name
in
[
f
.
name
for
f
in
autodiff_obj
.
forward_input_fields
]]
cls
.
forward_ast
=
forward_ast
cls
.
backward_ast
=
backward_ast
cls
.
num_regs
=
None
cls
.
call
=
call
cls
.
code
=
str
(
module
)
return
cls
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