From a547880856ec4c0c03e4edd4a8043aed70276fc8 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Tue, 9 Jan 2018 03:34:53 +0000 Subject: [PATCH] Deploy to GitHub Pages: 6ece41ecb92bab7c7615c8d46906dc1df2237a80 --- develop/doc/_sources/design/error_clip.md.txt | 87 +++++ develop/doc/design/error_clip.html | 343 +++++++++++++++++ develop/doc/objects.inv | Bin 3960 -> 3978 bytes develop/doc/searchindex.js | 2 +- .../doc_cn/_sources/design/error_clip.md.txt | 87 +++++ develop/doc_cn/design/error_clip.html | 357 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 4517 -> 4537 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 876 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/error_clip.md.txt create mode 100644 develop/doc/design/error_clip.html create mode 100644 develop/doc_cn/_sources/design/error_clip.md.txt create mode 100644 develop/doc_cn/design/error_clip.html diff --git a/develop/doc/_sources/design/error_clip.md.txt b/develop/doc/_sources/design/error_clip.md.txt new file mode 100644 index 0000000000..8e845462cc --- /dev/null +++ b/develop/doc/_sources/design/error_clip.md.txt @@ -0,0 +1,87 @@ +# Error Clip + +## Overview + +Error clip is widely used in model training to prevent gradient exploding. It takes some specific rules to adjust variables' gradients and prevent them from being too large. With it, values of a gradient will be checked before they are taken by the next `grad_op` and be shrunk if necessary. +## Usage + +Users are allowed to assign different error clip methods or attributes to different `Variable`s. Users can specify it as a parameter of `Variable`'s constructor: + +```python +var = framework.Variable(..., error_clip=myErrorClip, ...) +``` + +The default value of `error_clip` is `None`, which means no error clip is employed. When it's not `None`, it should take an object of `BaseErrorClipAttr`'s derived class. So far, `BaseErrorClipAttr` has only one derived class: `ErrorClipByValue`, whose constructor is: + +```python +ErrorClipByValue(max, min=None) +``` + +`max` and `min` represent the maximal and minimal clip threshold respectively. In backward pass, all values of `var`'s gradient greater than `max` or less than `min` will be clipped to `max` and `min` respectively. When the `min` is None, the minimal threshold will be assigned with `-max` automatically. + +So we can enable the error clip with threshold `[-5.0, 5.0]` for variable `var` by: + +```python +var = framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...) +``` + +## Implementation + +The `BaseErrorClipAttr` and its derived class `ErrorClipByValue` are defined in *clip.py*. + +```python +class BaseErrorClipAttr(object): + def append_clip_op(self, block, grad_name): + raise NotImplementedError() + + +class ErrorClipByValue(BaseErrorClipAttr): + def __init__(self, max, min=None): + max = float(max) + if min is None: + min = -max + else: + min = float(min) + self.max = max + self.min = min + + def append_clip_op(self, block, grad_name): + block.append_op( + type="clip", + inputs={"X": grad_name}, + outputs={"Out": grad_name}, + attrs={"min": self.min, + "max": self.max}) +``` + +The `BaseErrorClipAttr` have one main member functions: `append_clip_op(self, block, grad_name)`. + +This function is used to create a `clip_op` and append it to the end of given `block`. For different error clip algorithm require different `clip_op`, the function is defined as virtual in the base class. All derived classes must implement their own versions of this function. + +These `clip_op`s should be inserted after `grad_op`s whose output gradients need to be clipped. It is equivalent to appending some `clip_op`s to the end of the target block every time a new `grad_op` is added. + +```python +for op_desc in grad_op_descs: + new_op_desc = target_block.desc.append_op() + new_op_desc.copy_from(op_desc) + callback(block=target_block, context=grad_to_var) +``` + +Here we employ a callback function to complete this kind of jobs. In `_append_backward_ops_` function, each time after a `grad_op` is added to the `target_block`, a callback function is invoked. The logic of `clip_op` appending can be implemented inside the callback function. + +The callback function for `clip_op` appending is defined in *clip.py*: + +```python +def error_clip_callback(block, context): + # the context is a grad_to_var map + grad_to_var = context + op_desc = block.desc.op(block.desc.op_size() - 1) + for grad_n in filter(lambda n: grad_to_var.has_key(n), + op_desc.output_arg_names()): + fwd_var = block.var_recursive(grad_to_var[grad_n]) + error_clip = getattr(fwd_var, "error_clip", None) + if error_clip is not None: + error_clip.append_clip_op(block, grad_n) +``` + +This function takes a `block` and a `context`(which is actually a grad\_to\_var map) as inputs. It checks each output of the last `OpDesc` in the `block`. Notice that the last `OpDesc` of the `block` must be a `grad_op` and its outputs must be some forward variables' gradients. If an output gradient's corresponding forward variable has an attribute of `error_clip`, `error_clip_callback` will call the `error_clip`'s `append_clip_op` function to append the required `clip_op` into the `block`. diff --git a/develop/doc/design/error_clip.html b/develop/doc/design/error_clip.html new file mode 100644 index 0000000000..15d077efc1 --- /dev/null +++ b/develop/doc/design/error_clip.html @@ -0,0 +1,343 @@ + + + + + + + + + + + Error Clip — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
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+ + + +
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+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Error Clip
    • +
+
+ +
+
+
+
+ +
+

Error Clip

+
+

Overview

+

Error clip is widely used in model training to prevent gradient exploding. It takes some specific rules to adjust variables’ gradients and prevent them from being too large. With it, values of a gradient will be checked before they are taken by the next grad_op and be shrunk if necessary.

+
+
+

Usage

+

Users are allowed to assign different error clip methods or attributes to different Variables. Users can specify it as a parameter of Variable‘s constructor:

+
var = framework.Variable(..., error_clip=myErrorClip, ...)
+
+
+

The default value of error_clip is None, which means no error clip is employed. When it’s not None, it should take an object of BaseErrorClipAttr‘s derived class. So far, BaseErrorClipAttr has only one derived class: ErrorClipByValue, whose constructor is:

+
ErrorClipByValue(max, min=None)
+
+
+

max and min represent the maximal and minimal clip threshold respectively. In backward pass, all values of var‘s gradient greater than max or less than min will be clipped to max and min respectively. When the min is None, the minimal threshold will be assigned with -max automatically.

+

So we can enable the error clip with threshold [-5.0, 5.0] for variable var by:

+
var = framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...)
+
+
+
+
+

Implementation

+

The BaseErrorClipAttr and its derived class ErrorClipByValue are defined in clip.py.

+
class BaseErrorClipAttr(object):
+    def append_clip_op(self, block, grad_name):
+        raise NotImplementedError()
+
+
+class ErrorClipByValue(BaseErrorClipAttr):
+    def __init__(self, max, min=None):
+        max = float(max)
+        if min is None:
+            min = -max
+        else:
+            min = float(min)
+        self.max = max
+        self.min = min
+
+    def append_clip_op(self, block, grad_name):
+        block.append_op(
+            type="clip",
+            inputs={"X": grad_name},
+            outputs={"Out": grad_name},
+            attrs={"min": self.min,
+                   "max": self.max})
+
+
+

The BaseErrorClipAttr have one main member functions: append_clip_op(self, block, grad_name).

+

This function is used to create a clip_op and append it to the end of given block. For different error clip algorithm require different clip_op, the function is defined as virtual in the base class. All derived classes must implement their own versions of this function.

+

These clip_ops should be inserted after grad_ops whose output gradients need to be clipped. It is equivalent to appending some clip_ops to the end of the target block every time a new grad_op is added.

+
for op_desc in grad_op_descs:
+        new_op_desc = target_block.desc.append_op()
+        new_op_desc.copy_from(op_desc)
+        callback(block=target_block, context=grad_to_var)
+
+
+

Here we employ a callback function to complete this kind of jobs. In _append_backward_ops_ function, each time after a grad_op is added to the target_block, a callback function is invoked. The logic of clip_op appending can be implemented inside the callback function.

+

The callback function for clip_op appending is defined in clip.py:

+
def error_clip_callback(block, context):
+    # the context is a grad_to_var map
+    grad_to_var = context
+    op_desc = block.desc.op(block.desc.op_size() - 1)
+    for grad_n in filter(lambda n: grad_to_var.has_key(n),
+                         op_desc.output_arg_names()):
+        fwd_var = block.var_recursive(grad_to_var[grad_n])
+        error_clip = getattr(fwd_var, "error_clip", None)
+        if error_clip is not None:
+            error_clip.append_clip_op(block, grad_n)
+
+
+

This function takes a block and a context(which is actually a grad_to_var map) as inputs. It checks each output of the last OpDesc in the block. Notice that the last OpDesc of the block must be a grad_op and its outputs must be some forward variables’ gradients. If an output gradient’s corresponding forward variable has an attribute of error_clip, error_clip_callback will call the error_clip‘s append_clip_op function to append the required clip_op into the block.

+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/error_clip.md.txt b/develop/doc_cn/_sources/design/error_clip.md.txt new file mode 100644 index 0000000000..8e845462cc --- /dev/null +++ b/develop/doc_cn/_sources/design/error_clip.md.txt @@ -0,0 +1,87 @@ +# Error Clip + +## Overview + +Error clip is widely used in model training to prevent gradient exploding. It takes some specific rules to adjust variables' gradients and prevent them from being too large. With it, values of a gradient will be checked before they are taken by the next `grad_op` and be shrunk if necessary. +## Usage + +Users are allowed to assign different error clip methods or attributes to different `Variable`s. Users can specify it as a parameter of `Variable`'s constructor: + +```python +var = framework.Variable(..., error_clip=myErrorClip, ...) +``` + +The default value of `error_clip` is `None`, which means no error clip is employed. When it's not `None`, it should take an object of `BaseErrorClipAttr`'s derived class. So far, `BaseErrorClipAttr` has only one derived class: `ErrorClipByValue`, whose constructor is: + +```python +ErrorClipByValue(max, min=None) +``` + +`max` and `min` represent the maximal and minimal clip threshold respectively. In backward pass, all values of `var`'s gradient greater than `max` or less than `min` will be clipped to `max` and `min` respectively. When the `min` is None, the minimal threshold will be assigned with `-max` automatically. + +So we can enable the error clip with threshold `[-5.0, 5.0]` for variable `var` by: + +```python +var = framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...) +``` + +## Implementation + +The `BaseErrorClipAttr` and its derived class `ErrorClipByValue` are defined in *clip.py*. + +```python +class BaseErrorClipAttr(object): + def append_clip_op(self, block, grad_name): + raise NotImplementedError() + + +class ErrorClipByValue(BaseErrorClipAttr): + def __init__(self, max, min=None): + max = float(max) + if min is None: + min = -max + else: + min = float(min) + self.max = max + self.min = min + + def append_clip_op(self, block, grad_name): + block.append_op( + type="clip", + inputs={"X": grad_name}, + outputs={"Out": grad_name}, + attrs={"min": self.min, + "max": self.max}) +``` + +The `BaseErrorClipAttr` have one main member functions: `append_clip_op(self, block, grad_name)`. + +This function is used to create a `clip_op` and append it to the end of given `block`. For different error clip algorithm require different `clip_op`, the function is defined as virtual in the base class. All derived classes must implement their own versions of this function. + +These `clip_op`s should be inserted after `grad_op`s whose output gradients need to be clipped. It is equivalent to appending some `clip_op`s to the end of the target block every time a new `grad_op` is added. + +```python +for op_desc in grad_op_descs: + new_op_desc = target_block.desc.append_op() + new_op_desc.copy_from(op_desc) + callback(block=target_block, context=grad_to_var) +``` + +Here we employ a callback function to complete this kind of jobs. In `_append_backward_ops_` function, each time after a `grad_op` is added to the `target_block`, a callback function is invoked. The logic of `clip_op` appending can be implemented inside the callback function. + +The callback function for `clip_op` appending is defined in *clip.py*: + +```python +def error_clip_callback(block, context): + # the context is a grad_to_var map + grad_to_var = context + op_desc = block.desc.op(block.desc.op_size() - 1) + for grad_n in filter(lambda n: grad_to_var.has_key(n), + op_desc.output_arg_names()): + fwd_var = block.var_recursive(grad_to_var[grad_n]) + error_clip = getattr(fwd_var, "error_clip", None) + if error_clip is not None: + error_clip.append_clip_op(block, grad_n) +``` + +This function takes a `block` and a `context`(which is actually a grad\_to\_var map) as inputs. It checks each output of the last `OpDesc` in the `block`. Notice that the last `OpDesc` of the `block` must be a `grad_op` and its outputs must be some forward variables' gradients. If an output gradient's corresponding forward variable has an attribute of `error_clip`, `error_clip_callback` will call the `error_clip`'s `append_clip_op` function to append the required `clip_op` into the `block`. diff --git a/develop/doc_cn/design/error_clip.html b/develop/doc_cn/design/error_clip.html new file mode 100644 index 0000000000..d192b3fb39 --- /dev/null +++ b/develop/doc_cn/design/error_clip.html @@ -0,0 +1,357 @@ + + + + + + + + + + + Error Clip — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
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+
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+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Error Clip
    • +
+
+ +
+
+
+
+ +
+

Error Clip

+
+

Overview

+

Error clip is widely used in model training to prevent gradient exploding. It takes some specific rules to adjust variables’ gradients and prevent them from being too large. With it, values of a gradient will be checked before they are taken by the next grad_op and be shrunk if necessary.

+
+
+

Usage

+

Users are allowed to assign different error clip methods or attributes to different Variables. Users can specify it as a parameter of Variable‘s constructor:

+
var = framework.Variable(..., error_clip=myErrorClip, ...)
+
+
+

The default value of error_clip is None, which means no error clip is employed. When it’s not None, it should take an object of BaseErrorClipAttr‘s derived class. So far, BaseErrorClipAttr has only one derived class: ErrorClipByValue, whose constructor is:

+
ErrorClipByValue(max, min=None)
+
+
+

max and min represent the maximal and minimal clip threshold respectively. In backward pass, all values of var‘s gradient greater than max or less than min will be clipped to max and min respectively. When the min is None, the minimal threshold will be assigned with -max automatically.

+

So we can enable the error clip with threshold [-5.0, 5.0] for variable var by:

+
var = framework.Variable(..., error_clip=ErrorClipByValue(max=5.0), ...)
+
+
+
+
+

Implementation

+

The BaseErrorClipAttr and its derived class ErrorClipByValue are defined in clip.py.

+
class BaseErrorClipAttr(object):
+    def append_clip_op(self, block, grad_name):
+        raise NotImplementedError()
+
+
+class ErrorClipByValue(BaseErrorClipAttr):
+    def __init__(self, max, min=None):
+        max = float(max)
+        if min is None:
+            min = -max
+        else:
+            min = float(min)
+        self.max = max
+        self.min = min
+
+    def append_clip_op(self, block, grad_name):
+        block.append_op(
+            type="clip",
+            inputs={"X": grad_name},
+            outputs={"Out": grad_name},
+            attrs={"min": self.min,
+                   "max": self.max})
+
+
+

The BaseErrorClipAttr have one main member functions: append_clip_op(self, block, grad_name).

+

This function is used to create a clip_op and append it to the end of given block. For different error clip algorithm require different clip_op, the function is defined as virtual in the base class. All derived classes must implement their own versions of this function.

+

These clip_ops should be inserted after grad_ops whose output gradients need to be clipped. It is equivalent to appending some clip_ops to the end of the target block every time a new grad_op is added.

+
for op_desc in grad_op_descs:
+        new_op_desc = target_block.desc.append_op()
+        new_op_desc.copy_from(op_desc)
+        callback(block=target_block, context=grad_to_var)
+
+
+

Here we employ a callback function to complete this kind of jobs. In _append_backward_ops_ function, each time after a grad_op is added to the target_block, a callback function is invoked. The logic of clip_op appending can be implemented inside the callback function.

+

The callback function for clip_op appending is defined in clip.py:

+
def error_clip_callback(block, context):
+    # the context is a grad_to_var map
+    grad_to_var = context
+    op_desc = block.desc.op(block.desc.op_size() - 1)
+    for grad_n in filter(lambda n: grad_to_var.has_key(n),
+                         op_desc.output_arg_names()):
+        fwd_var = block.var_recursive(grad_to_var[grad_n])
+        error_clip = getattr(fwd_var, "error_clip", None)
+        if error_clip is not None:
+            error_clip.append_clip_op(block, grad_n)
+
+
+

This function takes a block and a context(which is actually a grad_to_var map) as inputs. It checks each output of the last OpDesc in the block. Notice that the last OpDesc of the block must be a grad_op and its outputs must be some forward variables’ gradients. If an output gradient’s corresponding forward variable has an attribute of error_clip, error_clip_callback will call the error_clip‘s append_clip_op function to append the required clip_op into the block.

+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
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+ +
+ +
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