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clip.py
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clip.py
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# Copyright (c) 2020 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 copy
import warnings
from sqlite3 import NotSupportedError
import paddle
import paddle.autograd as imperative_base
from paddle import _C_ops
from paddle.common_ops_import import Variable, check_type, default_main_program
from paddle.fluid import core, framework, unique_name
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.framework import LayerHelper, in_dynamic_mode
from paddle.tensor.layer_function_generator import templatedoc
__all__ = []
@templatedoc()
def clip_by_norm(x, max_norm, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
max_norm(${max_norm_type}): ${max_norm_comment}
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Returns:
Tensor:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
import paddle
from paddle.nn import clip
input = paddle.to_tensor([[2.0, 2.0], [2.0, 2.0]], dtype='float32')
reward = clip.clip_by_norm(x=input, max_norm=1.0)
# [[0.5, 0.5], [0.5, 0.5]]
"""
if in_dynamic_mode():
return _C_ops.clip_by_norm(x, max_norm)
helper = LayerHelper("clip_by_norm", **locals())
check_variable_and_dtype(
x, 'X', ['float16', 'float32', 'uint16'], 'clip_by_norm'
)
check_type(max_norm, 'max_norm', (float), 'clip_by_norm')
if name is None:
name = unique_name.generate_with_ignorable_key(
".".join([helper.name, 'tmp'])
)
out = helper.create_variable(
type=x.type, name=name, dtype=x.dtype, persistable=False
)
helper.append_op(
type="clip_by_norm",
inputs={"X": x},
attrs={"max_norm": max_norm},
outputs={"Out": out},
)
return out
@templatedoc()
def merge_selected_rows(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
import paddle.fluid as fluid
b = fluid.default_main_program().global_block()
var = b.create_var(
name="X", dtype="float32", persistable=True,
type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
y = nn.merge_selected_rows(var)
"""
if in_dynamic_mode():
return _C_ops.merge_selected_rows(x)
helper = LayerHelper("merge_selected_rows", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="merge_selected_rows",
inputs={"X": x},
attrs={},
outputs={"Out": out},
)
return out
@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
"""
Get tensor data from input with SelectedRows type, and outputs a Tensor.
.. code-block:: text
input x is SelectedRows:
x.rows = [0, 5, 5, 4, 19]
x.height = 20
x.value = [[1, 1] [2, 2] [2, 2] [3, 3] [6, 6]]
Output is LoDTensor:
out.shape = [5, 2]
out.data = [[1, 1],
[2, 2],
[2, 2],
[3, 3],
[6, 6]]
Args:
x(SelectedRows): Input with SelectedRows type. The data type is float32, float64, int32 or int64.
name(str, optional): The default value is None. Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Returns:
Variable: LoDTensor transformed from SelectedRows. The data type is same with input.
Examples:
.. code-block:: python
from paddle import nnp.py
b = fluid.default_main_program().global_block()
input = b.create_var(name="X", dtype="float32", persistable=True, type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
out = nn.get_tensor_from_selected_rows(input)
"""
check_type(x, 'x', Variable, 'get_tensor_from_selected_rows')
if x.type != core.VarDesc.VarType.SELECTED_ROWS:
raise TypeError(
"The type of 'x' in get_tensor_from_selected_rows must be SELECTED_ROWS."
)
helper = LayerHelper('get_tensor_from_selected_rows', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='get_tensor_from_selected_rows',
inputs={'X': x},
outputs={'Out': out},
attrs={},
)
return out
_clip_by_global_norm_using_mp_type_flag = False
def _clip_by_global_norm_using_mp_type(*args):
global _clip_by_global_norm_using_mp_type_flag
assert len(args) <= 1
if len(args) == 1:
assert isinstance(args[0], bool)
old_value = _clip_by_global_norm_using_mp_type_flag
_clip_by_global_norm_using_mp_type_flag = args[0]
return old_value
else:
return _clip_by_global_norm_using_mp_type_flag
def _cast_to_mp_type_if_enabled(x):
if (
x.dtype == core.VarDesc.VarType.FP16
or x.dtype == core.VarDesc.VarType.BF16
) and _clip_by_global_norm_using_mp_type():
return x.astype(core.VarDesc.VarType.FP32)
else:
return x
def _squared_l2_norm(x):
r"""
Return the squared L2 norm of a tensor.
"""
x = _cast_to_mp_type_if_enabled(x)
if core.is_compiled_with_xpu():
square = paddle.square(x)
sum_square = paddle.sum(square)
return sum_square
if in_dynamic_mode():
return _C_ops.squared_l2_norm(x)
op_type = 'squared_l2_norm'
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'float16', 'uint16'], op_type
)
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(x.dtype)
inputs = {"X": x}
outputs = {'Out': out}
helper.append_op(type=op_type, inputs=inputs, outputs=outputs)
return out
class BaseErrorClipAttr:
def __str__(self):
raise NotImplementedError()
def _append_clip_op(self, block, grad_name):
raise NotImplementedError()
class ErrorClipByValue(BaseErrorClipAttr):
r"""
Clip tensor values to the range [min, max].
Given a tensor ``t`` (see Examples below), this operation clips its value \
to ``min`` and ``max`` inplace.
- Any values less than min are set to min.
- Any values greater than max are set to max.
Args:
max (float): The maximum value to clip by.
min (float, optional): The minimum value to clip by. if not set by user, \
will be set to ``-max`` by framework.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
paddle.enable_static()
BATCH_SIZE = 128
CLIP_MAX = 2e-6
CLIP_MIN = -1e-6
prog = fluid.framework.Program()
with fluid.program_guard(main_program=prog):
image = fluid.layers.data(
name='x', shape=[784], dtype='float32')
hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(
input=hidden2, size=10, act='softmax')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = paddle.nn.functional.cross_entropy(input=predict, label=label)
avg_cost = paddle.mean(cost)
prog_clip = prog.clone()
prog_clip.block(0).var(hidden1.name)._set_error_clip(
paddle.nn.clip.ErrorClipByValue(
max=CLIP_MAX, min=CLIP_MIN)
)
"""
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 __str__(self):
return f"ByValue, min={self.min:f}, max={self.max:f}"
def _append_clip_op(self, block, grad_name):
clip_op_desc = block.desc.append_op()
clip_op_desc.set_type("clip")
clip_op_desc.set_input("X", [grad_name])
clip_op_desc.set_output("Out", [grad_name])
clip_op_desc._set_attr("min", self.min)
clip_op_desc._set_attr("max", self.max)
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 [n for n in op_desc.output_arg_names() if n in grad_to_var]:
fwd_var = block._var_recursive(grad_to_var[grad_n])
error_clip = getattr(fwd_var, "error_clip", None)
if not (
error_clip is None or isinstance(error_clip, BaseErrorClipAttr)
):
raise TypeError(
"Variable's error_clip should be an instance of BaseErrorClipAttr or None."
)
if error_clip is not None:
error_clip._append_clip_op(block, grad_n)
class ClipGradBase:
def __init__(self):
super().__init__()
def __str__(self):
raise NotImplementedError()
@imperative_base.no_grad()
def _dygraph_clip(self, params_grads):
raise NotImplementedError
def _static_clip(self, params_grads):
raise NotImplementedError
def __call__(self, params_grads):
if in_dynamic_mode():
return self._dygraph_clip(params_grads)
else:
for p, g in params_grads:
if getattr(p, 'gradient_clip_attr', None) is not None:
warnings.warn(
"'set_gradient_clip' will be ineffective, because you have "
"set 'need_clip' in 'ParamAttr'. So, 'set_gradient_clip' "
"is redundant and you can remove it."
)
break
return self._static_clip(params_grads)
def _process_context(self, context, param, grad):
raise NotImplementedError()
def _create_operators(self, param, grad):
raise NotImplementedError()
class ClipGradByValue(ClipGradBase):
"""
Limit the value of multi-dimensional Tensor :math:`X` to the range [min, max].
- Any values less than min are set to ``min``.
- Any values greater than max are set to ``max``.
The multi-dimensional Tensor :math:`X` is not passed from this class, but the gradients of all parameters set in ``optimizer``.
If ``need_clip`` of specific param is ``False`` in its ``ParamAttr``, then the gradients of this param will not be clipped.
Gradient clip will takes effect after being set in ``optimizer`` , see the document ``optimizer``
(for example: :ref:`api_paddle_optimizer_SGD`).
Note:
``need_clip`` of ``ClipGradByValue`` HAS BEEN DEPRECATED since 2.0.
Please use ``need_clip`` in ``ParamAttr`` to speficiy the clip scope.
Args:
max (float): The maximum value to clip by.
min (float, optional): The minimum value to clip by. if not set by user, it will be set to ``-max``
automatically. In this case, ``max`` must be greater than :math:`0`.
Examples:
.. code-block:: python
import paddle
x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
linear = paddle.nn.Linear(in_features=10, out_features=10,
weight_attr=paddle.ParamAttr(need_clip=True),
bias_attr=paddle.ParamAttr(need_clip=False))
out = linear(x)
loss = paddle.mean(out)
loss.backward()
clip = paddle.nn.ClipGradByValue(min=-1, max=1)
sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
sdg.step()
"""
def __init__(self, max, min=None):
super().__init__()
if min is None:
assert max > 0.0
min = -max
self.max = float(max)
self.min = float(min)
def __str__(self):
return f"Clip Gradient By Value, min = {self.min:f}, max={self.max:f}"
@imperative_base.no_grad()
def _dygraph_clip(self, params_grads):
params_and_grads = []
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
new_grad = paddle.clip(x=g, min=self.min, max=self.max)
params_and_grads.append((p, new_grad))
return params_and_grads
def _static_clip(self, params_grads):
params_and_grads = []
param_new_grad_name_dict = {}
with framework.name_scope('gradient_clip'):
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
with p.block.program._optimized_guard([p, g]):
new_grad = paddle.clip(x=g, min=self.min, max=self.max)
params_and_grads.append((p, new_grad))
param_new_grad_name_dict[p.name] = new_grad.name
_correct_clip_op_role_var(params_and_grads, param_new_grad_name_dict)
return params_and_grads
def _process_context(self, context, param, grad):
pass
def _create_operators(self, param, grad):
new_grad = paddle.clip(x=grad, min=self.min, max=self.max)
return param, new_grad
class ClipGradByNorm(ClipGradBase):
r"""
Limit the l2 norm of multi-dimensional Tensor :math:`X` to ``clip_norm`` .
- If the l2 norm of :math:`X` is greater than ``clip_norm`` , :math:`X` will be compressed by a ratio.
- If the l2 norm of :math:`X` is less than or equal to ``clip_norm`` , nothing will be done.
The multidimensional Tensor :math:`X` is not passed from this class, but the gradients of all parameters set in ``optimizer``.
If ``need_clip`` of specific param is ``False`` in its ``ParamAttr``, then the gradients of this param will not be clipped.
Gradient clip will takes effect after being set in ``optimizer`` , see the document ``optimizer``
(for example: :ref:`api_paddle_optimizer_SGD`).
The clipping formula is:
.. math::
Out =
\left\{
\begin{array}{ccl}
X & & if (norm(X) \leq clip\_norm) \\
\frac{clip\_norm*X}{norm(X)} & & if (norm(X) > clip\_norm) \\
\end{array}
\right.
where :math:`norm(X)` represents the L2 norm of :math:`X`.
.. math::
norm(X) = ( \sum_{i=1}^{n}|x\_i|^2)^{ \frac{1}{2}}
Note:
``need_clip`` of ``ClipGradByNorm`` HAS BEEN DEPRECATED since 2.0.
Please use ``need_clip`` in ``ParamAttr`` to speficiy the clip scope.
Args:
clip_norm(float): The maximum norm value.
Examples:
.. code-block:: python
import paddle
x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
linear = paddle.nn.Linear(in_features=10, out_features=10,
weight_attr=paddle.ParamAttr(need_clip=True),
bias_attr=paddle.ParamAttr(need_clip=False))
out = linear(x)
loss = paddle.mean(out)
loss.backward()
clip = paddle.nn.ClipGradByNorm(clip_norm=1.0)
sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
sdg.step()
"""
def __init__(self, clip_norm):
super().__init__()
self.clip_norm = float(clip_norm)
def __str__(self):
return "Gradient Clip By Norm, clip_norm=%f" % self.clip_norm
@imperative_base.no_grad()
def _dygraph_clip(self, params_grads):
params_and_grads = []
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
new_grad = clip_by_norm(x=g, max_norm=self.clip_norm)
params_and_grads.append((p, new_grad))
return params_and_grads
def _static_clip(self, params_grads):
params_and_grads = []
with framework.name_scope('gradient_clip'):
param_new_grad_name_dict = {}
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
with p.block.program._optimized_guard([p, g]):
new_grad = clip_by_norm(x=g, max_norm=self.clip_norm)
param_new_grad_name_dict[p.name] = new_grad.name
params_and_grads.append((p, new_grad))
_correct_clip_op_role_var(params_and_grads, param_new_grad_name_dict)
return params_and_grads
def _process_context(self, context, param, grad):
pass
def _create_operators(self, param, grad):
new_grad = clip_by_norm(x=grad, max_norm=self.clip_norm)
return param, new_grad
_allow_pure_fp16_global_norm_clip_flag = False
def _allow_pure_fp16_global_norm_clip(*args):
global _allow_pure_fp16_global_norm_clip_flag
if len(args) == 0:
return _allow_pure_fp16_global_norm_clip_flag
else:
assert len(args) == 1 and isinstance(args[0], bool)
old_value = _allow_pure_fp16_global_norm_clip_flag
_allow_pure_fp16_global_norm_clip_flag = args[0]
return old_value
_allow_pure_bf16_global_norm_clip_flag = False
def _allow_pure_bf16_global_norm_clip(*args):
global _allow_pure_bf16_global_norm_clip_flag
if len(args) == 0:
return _allow_pure_bf16_global_norm_clip_flag
else:
assert len(args) == 1 and isinstance(args[0], bool)
old_value = _allow_pure_bf16_global_norm_clip_flag
_allow_pure_bf16_global_norm_clip_flag = args[0]
return old_value
class ClipGradByGlobalNorm(ClipGradBase):
r"""
Given a list of Tensor :math:`t\_list` , calculate the global norm for the elements of all tensors in
:math:`t\_list` , and limit it to ``clip_norm`` .
- If the global norm is greater than ``clip_norm`` , all elements of :math:`t\_list` will be compressed by a ratio.
- If the global norm is less than or equal to ``clip_norm`` , nothing will be done.
The list of Tensor :math:`t\_list` is not passed from this class, but the gradients of all parameters set in ``optimizer``.
If ``need_clip`` of specific param is ``False`` in its ``ParamAttr``, then the gradients of this param will not be clipped.
Gradient clip will takes effect after being set in ``optimizer`` , see the document ``optimizer``
(for example: :ref:`api_paddle_optimizer_SGD`).
The clipping formula is:
.. math::
t\_list[i] = t\_list[i] * \frac{clip\_norm}{\max(global\_norm, clip\_norm)}
where:
.. math::
global\_norm = \sqrt{\sum_{i=0}^{N-1}(l2norm(t\_list[i]))^2}
Note:
``need_clip`` of ``ClipGradyGlobalNorm`` HAS BEEN DEPRECATED since 2.0.
Please use ``need_clip`` in ``ParamAttr`` to speficiy the clip scope.
Args:
clip_norm (float): The maximum norm value.
group_name (str, optional): The group name for this clip. Default value is ``default_group``.
auto_skip_clip (bool, optional): skip clipping gradient. Default value is ``False``.
Examples:
.. code-block:: python
import paddle
x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
linear = paddle.nn.Linear(in_features=10, out_features=10,
weight_attr=paddle.ParamAttr(need_clip=True),
bias_attr=paddle.ParamAttr(need_clip=False))
out = linear(x)
loss = paddle.mean(out)
loss.backward()
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
sdg.step()
"""
def __init__(
self, clip_norm, group_name="default_group", auto_skip_clip=False
):
super().__init__()
self.clip_norm = float(clip_norm)
self.group_name = group_name
assert isinstance(auto_skip_clip, bool)
self.auto_skip_clip = auto_skip_clip
def __str__(self):
return "Gradient Clip By GlobalNorm, global_norm=%f" % (self.clip_norm)
@imperative_base.no_grad()
def _dygraph_clip(self, params_grads):
params_and_grads = []
sum_square_list = []
sum_square_list_fp16 = []
sum_square_list_fp32 = []
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
continue
merge_grad = g
if in_dynamic_mode() and g.is_selected_rows():
merge_grad = merge_selected_rows(g)
merge_grad = merge_grad._get_tensor_from_selected_rows()
elif g.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = merge_selected_rows(g)
merge_grad = get_tensor_from_selected_rows(merge_grad)
sum_square = _squared_l2_norm(merge_grad)
if (
sum_square.dtype == core.VarDesc.VarType.FP16
or sum_square.dtype == core.VarDesc.VarType.BF16
):
sum_square_list_fp16.append(sum_square)
elif sum_square.dtype == core.VarDesc.VarType.FP32:
sum_square_list_fp32.append(sum_square)
else:
sum_square_list.append(sum_square)
# all parameters have been filterd out
if (
len(sum_square_list)
+ len(sum_square_list_fp16)
+ len(sum_square_list_fp32)
== 0
):
return params_grads
sum_dtype = 'float64' if len(sum_square_list) > 0 else "float32"
global_norm_var = []
if len(sum_square_list_fp16) > 0:
global_norm_var_fp16 = paddle.add_n(sum_square_list_fp16)
global_norm_var.append(global_norm_var_fp16.astype(sum_dtype))
if len(sum_square_list_fp32) > 0:
global_norm_var_fp32 = paddle.add_n(sum_square_list_fp32)
if sum_dtype == 'float32':
global_norm_var.append(global_norm_var_fp32)
else:
global_norm_var.append(global_norm_var_fp32.astype(sum_dtype))
if len(sum_square_list) > 0:
global_norm_var_fp64 = paddle.add_n(sum_square_list)
global_norm_var.append(global_norm_var_fp64)
global_norm_var = paddle.add_n(global_norm_var)
global_norm_var = paddle.sqrt(global_norm_var)
max_global_norm = paddle.full(
shape=[], dtype=global_norm_var.dtype, fill_value=self.clip_norm
)
need_clip = False
if not self.auto_skip_clip: # always apply clip
need_clip = True
clip_var = paddle.divide(
x=max_global_norm,
y=paddle.maximum(x=global_norm_var, y=max_global_norm),
)
elif global_norm_var > max_global_norm:
# only when global_norm_var > max_global_norm, grad need clip
need_clip = True
clip_var = paddle.divide(x=max_global_norm, y=global_norm_var)
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
# TODO(wangxi): use inplace elementwise_mul
if need_clip:
clip_input = (
clip_var.astype(g.dtype)
if clip_var.dtype != g.dtype
else clip_var
)
new_grad = paddle.multiply(g, clip_input)
params_and_grads.append((p, new_grad))
else:
params_and_grads.append((p, g))
return params_and_grads
def _static_clip(self, params_grads):
params_and_grads = []
sum_square_list = []
sum_square_list_fp16 = []
sum_square_list_bf16 = []
sum_square_list_fp32 = []
with framework.name_scope('gradient_clip'):
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
continue
merge_grad = g
with p.block.program._optimized_guard([p, g]):
if g.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = merge_selected_rows(g)
merge_grad = get_tensor_from_selected_rows(merge_grad)
sum_square = _squared_l2_norm(merge_grad)
if sum_square.dtype == core.VarDesc.VarType.FP16:
sum_square_list_fp16.append(sum_square)
elif sum_square.dtype == core.VarDesc.VarType.BF16:
sum_square_list_bf16.append(sum_square)
elif sum_square.dtype == core.VarDesc.VarType.FP32:
sum_square_list_fp32.append(sum_square)
else:
sum_square_list.append(sum_square)
if len(sum_square_list_fp16) > 0 and len(sum_square_list_bf16) > 0:
raise NotSupportedError(
'FP16 and BF16 are not supported at the same time.'
)
# all parameters have been filterd out
if (
len(sum_square_list)
+ len(sum_square_list_fp16)
+ len(sum_square_list_fp32)
== 0
) and (
len(sum_square_list)
+ len(sum_square_list_bf16)
+ len(sum_square_list_fp32)
== 0
):
return params_grads
with p.block.program._optimized_guard([p, g]):
sum_dtype = 'float64' if len(sum_square_list) > 0 else "float32"
global_norm_var = []
if len(sum_square_list_fp16) > 0:
global_norm_var_fp16 = paddle.add_n(sum_square_list_fp16)
if (
sum_square_list_fp32
or sum_square_list
or not _allow_pure_fp16_global_norm_clip()
):
global_norm_var.append(
global_norm_var_fp16.astype(sum_dtype)
)
else:
global_norm_var.append(global_norm_var_fp16)
if len(sum_square_list_bf16) > 0:
global_norm_var_bf16 = paddle.add_n(sum_square_list_bf16)
if (
sum_square_list_fp32
or sum_square_list
or not _allow_pure_bf16_global_norm_clip()
):
global_norm_var.append(
global_norm_var_bf16.astype(sum_dtype)
)
else:
global_norm_var.append(global_norm_var_bf16)
if len(sum_square_list_fp32) > 0:
global_norm_var_fp32 = paddle.add_n(sum_square_list_fp32)
if sum_dtype == 'float32':
global_norm_var.append(global_norm_var_fp32)
else:
global_norm_var.append(
global_norm_var_fp32.astype(sum_dtype)
)
if len(sum_square_list) > 0:
# fp64
global_norm_var_other_dtype = paddle.add_n(sum_square_list)
global_norm_var.append(global_norm_var_other_dtype)
global_norm_var = (
paddle.add_n(global_norm_var)
if len(global_norm_var) > 1
else global_norm_var[0]
)
global_norm_var = paddle.sqrt(x=global_norm_var)
max_global_norm = paddle.full(
shape=[1],
dtype=global_norm_var.dtype,
fill_value=self.clip_norm,
)
scale_var = paddle.divide(
x=max_global_norm,
y=paddle.maximum(x=max_global_norm, y=global_norm_var),
)
param_new_grad_name_dict = {}
for p, g in params_grads:
if g is None:
continue
if getattr(p, 'need_clip', True) is False:
params_and_grads.append((p, g))
continue
with p.block.program._optimized_guard([p, g]):
new_g = _cast_to_mp_type_if_enabled(g)
# inplace
if (
new_g.dtype == core.VarDesc.VarType.FP16
and scale_var.dtype != core.VarDesc.VarType.FP16
):
scale_input = scale_var.astype('float16')
elif (
new_g.dtype == core.VarDesc.VarType.BF16
and scale_var.dtype != core.VarDesc.VarType.BF16
):
scale_input = scale_var.astype('bfloat16')
else:
scale_input = scale_var
# NOTE(Yuang Liu): For pure dp with gradient merge, the p and g
# will be in different blocks with the gradient clip related ops.
# We need to handle the correct block, otherwise will encounter
# a 'NotFoundError' during compile time.
block = default_main_program().current_block()
block.append_op(
type='elementwise_mul',
inputs={'X': new_g, 'Y': scale_input},
outputs={'Out': new_g},
)
if new_g is not g:
block.append_op(
type='cast',
inputs={'X': new_g},
outputs={'Out': g},
attrs={
'in_dtype': new_g.dtype,
'out_dtype': g.dtype,
},
)
param_new_grad_name_dict[p.name] = g.name
params_and_grads.append((p, g))
_correct_clip_op_role_var(params_and_grads, param_new_grad_name_dict)
return params_and_grads
def _process_context(self, context, param, grad):
if self.group_name not in context:
context[self.group_name] = []
context[self.group_name + "_clip_value"] = self.clip_norm
context[self.group_name + "_clip"] = paddle.full(
shape=[1], dtype=grad.dtype, fill_value=self.clip_norm
)
else:
if not self.clip_norm == context[self.group_name + "_clip_value"]:
raise ValueError(
"All parameters' 'clip_norm' of a same group should be the same"
)
merge_grad = grad
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = merge_selected_rows(grad)
merge_grad = get_tensor_from_selected_rows(merge_grad)
local_norm_var = _squared_l2_norm(merge_grad)
context[self.group_name].append(local_norm_var)
self.context = context
def _create_operators(self, param, grad):
group_scale_name = self.group_name + "_scale"
if group_scale_name not in self.context:
group_norm_var = paddle.add_n(self.context[self.group_name])
group_norm_var = paddle.sqrt(x=group_norm_var)
clip_var = self.context[self.group_name + "_clip"]
group_scale_var = paddle.divide(
x=clip_var,
y=paddle.maximum(x=clip_var, y=group_norm_var),
)
assert group_scale_var.shape == (1,)
self.context[group_scale_name] = group_scale_var
# inplace
param.block.append_op(
type='elementwise_mul',
inputs={'X': grad, 'Y': self.context[group_scale_name]},
outputs={'Out': grad},
)
return param, grad
@framework.dygraph_not_support
def set_gradient_clip(clip, param_list=None, program=None):
"""
Warning:
This API must be used after building network, and before ``minimize`` ,
and it may be removed in future releases, so it is not recommended.
It is recommended to set ``grad_clip`` when initializing the ``optimizer`` ,
this is a better method to clip gradient. There are three clipping strategies:
:ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` .
To specify parameters that require gradient clip.
Args:
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default value: None, and there is no
gradient clipping.
param_list (list(Variable), optional): Parameters that require gradient clip.
It can be a list of parameter or a list of parameter's name.
Default None, meaning that all parameters in the program will be included.
program (Program, optional): The program where parameters are located.
Default None, meaning that using :ref:`api_fluid_default_main_program` .
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
def network():
image = paddle.static.data(name='image', shape=[
None, 28], dtype='float32')
param_attr1 = fluid.ParamAttr("fc1_param")
fc1 = fluid.layers.fc(image, size=10, param_attr=param_attr1)
param_attr2 = fluid.ParamAttr("fc2_param")
fc2 = fluid.layers.fc(fc1, size=10, param_attr=param_attr2)
loss = paddle.mean(fc2)
return loss
# network 1: clip all parameter gradient
with fluid.program_guard(fluid.Program(), fluid.Program()):
loss = network()
paddle.nn.clip.set_gradient_clip(
paddle.nn.ClipGradByGlobalNorm(clip_norm=2.0))
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(loss)
# network 2: clip parameter gradient by name
with fluid.program_guard(fluid.Program(), fluid.Program()):
loss = network()
paddle.nn.clip.set_gradient_clip(
paddle.nn.ClipGradByValue(min=-1.0, max=1.0),
param_list=["fc1_param", "fc2_param"])
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(loss)
# network 3: clip parameter gradient by value
with fluid.program_guard(fluid.Program(), fluid.Program()):
loss = network()
param_var1 = fluid.default_main_program().global_block().var("fc1_param")
param_var2 = fluid.default_main_program().global_block().var("fc2_param")