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体验新版 GitCode,发现更多精彩内容 >>
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d7b15935
编写于
1月 15, 2019
作者:
X
Xin Pan
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add more doc
test=develop
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7 deletion
+26
-7
paddle/fluid/imperative/README.md
paddle/fluid/imperative/README.md
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paddle/fluid/imperative/README.md
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d7b15935
...
...
@@ -38,8 +38,6 @@ class PyLayer(core.PyLayer):
def
backward
(
inputs
):
# any backward logic implemented with numpy io.
```
...
...
@@ -62,9 +60,13 @@ class IVariable(PyVarBase):
def
__init__
(
self
):
self
.
_ivar
=
core
.
VarBase
()
# Move var to a device.
def
to
(
device
):
pass
# Get var value.
def
value
():
pass
# Trigger backward.
def
backward
():
pass
# Get var's gradient value.
def
gradient_value
():
pass
# operators to override.
```
...
...
@@ -100,18 +102,22 @@ Lots of research already.
https://autodiff-workshop.github.io/
https://en.wikipedia.org/wiki/Automatic_differentiation
## Execution Engine
Basically, trace the forward execution, and perform autodiff
when needed.
Lazy execution of pushed C++ operations.
*
Can be triggered by
`backward()`
.
*
Can select a block of code to trace and autodiff.
*
Use
`require_grad`
to drop some forward subgraph that doesn't need autodiff.
##
Tests
##
Execution Engine
*
All op tests run once in static graph, once in imperative mode
.
Lazy execution of pushed C++ operations
.
## Refactor
*
All function layers with parameters converted to class Layers.
*
Models converted to imperative mode.
*
Existing models converted to imperative mode.
*
All op tests run once in static graph, once in imperative mode.
# Examples
...
...
@@ -140,6 +146,15 @@ class MyPyLayer(fluid.imperative.PyLayer):
return
np
.
array
(
dout
)
*
(
1
-
np
.
square
(
np
.
array
(
out
)))
np_inp
=
np
.
ones
([
2
,
2
],
np
.
float32
)
with
fluid
.
imperative
.
guard
():
my_py_layer
=
MyPyLayer
()
outs
=
my_py_layer
(
np_inp
)
dy_out
=
np
.
sum
(
outs
[
0
].
_numpy
())
outs
[
0
].
_backward
()
dy_grad
=
var_inp
.
_gradient
()
class
MLP
(
fluid
.
imperative
.
Layer
):
def
__init__
(
self
):
super
(
MLP
,
self
).
__init__
()
...
...
@@ -171,6 +186,10 @@ class MLP(fluid.imperative.Layer):
TODO
## I/O
TODO
# Plan
2.
1,3 fulltime, Can run a few simple models. (Currently, 2 20% engs)
...
...
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