"Current train mode is pure fp16, models should be paddle.nn.Layer, but receive {}.".format(
type(model)
)
)
ifisinstance(model,paddle.DataParallel):
raiseRuntimeError(
"For distributed AMP training, you should first use paddle.amp.decorate() to decotate origin model, and then call paddle.DataParallel get distributed model."
"Current train mode is pure fp16, optimizers should be paddle.optimizer.Optimizer or paddle.fluid.optimizer.Optimizer or DygraphShardingOptimizer, but receive {}.".format(
type(optimizer)
)
)
@signature_safe_contextmanager
@dygraph_only
defamp_guard(
enable=True,
custom_white_list=None,
custom_black_list=None,
level='O1',
dtype='float16',
):
"""
Create a context which enables auto-mixed-precision(AMP) of operators executed in dynamic graph mode.
If enabled, the input data type (float32 or float16) of each operator is decided
by autocast algorithm for better performance.
Commonly, it is used together with `GradScaler` to achieve Auto-Mixed-Precision in
imperative mode. It is used together with `decorator` to achieve Pure fp16 in imperative mode.
Args:
enable(bool, optional): Enable auto-mixed-precision or not. Default is True.
custom_white_list(set|list|tuple, optional): The custom white_list. It's the set of ops that support
fp16 calculation and are considered numerically-safe and performance-critical. These ops
will be converted to fp16.
custom_black_list(set|list|tuple, optional): The custom black_list. The set of ops that support fp16
calculation and are considered numerically-dangerous and whose effects may also be
observed in downstream ops. These ops will not be converted to fp16.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the input data type of each operator will be casted by white_list and black_list;
O2 represent Pure fp16, all operators parameters and input data will be casted to fp16, except operators in black_list, don't support fp16 kernel and batchnorm. Default is O1(amp)
dtype(str, optional): Whether to use 'float16' or 'bfloat16'. Default is 'float16'.
Examples:
.. code-block:: python
import numpy as np
import paddle
data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
conv2d = paddle.nn.Conv2D(3, 2, 3)
data = paddle.to_tensor(data)
with paddle.amp.amp_guard():
conv = conv2d(data)
print(conv.dtype) # FP16
with paddle.amp.amp_guard(enable=False):
conv = conv2d(data)
print(conv.dtype) # FP32
"""
amp_state=locals()
global_g_amp_state_
original_state=_g_amp_state_
_g_amp_state_=amp_state
# check amp_level: O0-O2
level=level.upper()
ifnot(levelin['O0','O1','O2']):
raiseValueError(
"level should be O0, O1 or O2. O0 represents fp32 train mode, O1 represents AMP train mode, O2 represents pure fp16/bf16 train mode."
)
# check amp_dtype: float16 or bfloat16
dtype=dtype.lower()
ifnot(dtypein['float16','bfloat16']):
raiseValueError("dtype should be 'float16' or 'bfloat16'.")
# check tracer
tracer=_dygraph_tracer()
ifnottracer:
raiseValueError(
"current_tracer is None, maybe it is not in imperative mode."
)
# check device_type:
# NOTE: Now, amp only support gpu for float16 and bfloat16, xpu for float16, mlu for float16, npu for float16.
# Maybe we will support cpu for bfloat16.
ifenableandnot(
tracer._expected_place.is_gpu_place()
ortracer._expected_place.is_xpu_place()
ortracer._expected_place.is_mlu_place()
ortracer._expected_place.is_npu_place()
ortracer._expected_place.is_custom_place()
):
warnings.warn(
'amp_guard can only be enabled on CUDAPlace, XPUPlace, MLUPlace, NPUPlace, and CustomPlace, current place is %s, so it makes no effect.'
"For bfloat16, amp only support NVIDIA GPU with Compute Capability 8.0 or higher and CUDA Version 11.0 or higher, current GPU is: %s, with Compute Capability: %d.%d, current CUDA Version is: %s."
Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm.
Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode.
Args:
models(Layer|list of Layer, optional): The defined models by user, models must be either a single model or a list of models. Default is None.
optimizers(Optimizer|list of Optimizer, optional): The defined optimizers by user, optimizers must be either a single optimizer or a list of optimizers. Default is None.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the decorator will do nothing;
O2 represent Pure fp16/bf16, the decorator will cast all parameters of models to FP16/BF16, except BatchNorm and LayerNorm. Default is O1(amp)
dtype(str, optional): Whether to use 'float16' or 'bfloat16'. Default is 'float16'.
master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None.
save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, bfloat16, float32, float64 or None.
The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None.
Examples:
.. code-block:: python
__all__=[]
# required: gpu
# Demo1: single model and optimizer:
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
AmpScaler is used for Auto-Mixed-Precision training/inferring in imperative
mode. It controls the scaling of loss, helps avoiding numerical overflow.
The object of this class has seventeen methods `scale()`, `unscale_()`, `minimize()` and `get`/`set` api of parameters.
`scale()` is used to multiply the loss by a scale ratio.
`unscale_()` is used to unscale the gradients of parameters, multiplies the gradients of parameters by 1/(scale ratio)
`minimize()` is similar as `optimizer.minimize()`, performs parameters updating, and it will update the loss_scaling.
Commonly, it is used together with `amp_guard` to achieve Auto-Mixed-Precision in
imperative mode.
Args:
enable(bool, optional): Enable loss scaling or not. Default is True.
init_loss_scaling (float, optional): The initial loss scaling factor. Default is 2**15.
incr_ratio(float, optional): The multiplier to use when increasing the loss
scaling. Default is 2.0.
decr_ratio(float, optional): The less-than-one-multiplier to use when decreasing
the loss scaling. Default is 0.5.
incr_every_n_steps(int, optional): Increases loss scaling every n consecutive
steps with finite gradients. Default is 1000.
decr_every_n_nan_or_inf(int, optional): Decreases loss scaling every n
accumulated steps with nan or inf gradients. Default is 2.
use_dynamic_loss_scaling(bool, optional): Whether to use dynamic loss scaling. If False, fixed loss_scaling is used. If True, the loss scaling is updated dynamicly. Default is True.
Returns:
An AmpScaler object.
Examples:
.. code-block:: python
import numpy as np
import paddle
data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
Set the num `n` by `new_incr_every_n_steps`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
Args:
new_incr_every_n_steps(int): The new_incr_every_n_steps used to update the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
"""
self._incr_every_n_steps=new_incr_every_n_steps
defget_decr_every_n_nan_or_inf(self):
"""
Return the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Reurns:
int: the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Set the num `n` by `new_decr_every_n_nan_or_inf`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Args:
new_decr_every_n_nan_or_inf(int): The new_decr_every_n_nan_or_inf used to update the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Returns the state of the scaler as a `dict`, If this instance is not enabled, returns an empty dict.
Reurns:
A dict of scaler includes:
scale (tensor): The loss scaling factor.
incr_ratio(float): The multiplier to use when increasing the loss scaling.
decr_ratio(float): The less-than-one-multiplier to use when decreasing the loss scaling.
incr_every_n_steps(int): Increases loss scaling every n consecutive steps with finite gradients.
decr_every_n_nan_or_inf(int): Decreases loss scaling every n accumulated steps with nan or inf gradients.
incr_count(int): The number of recent consecutive unskipped steps.
decr_count(int): The number of recent consecutive skipped steps.
use_dynamic_loss_scaling(bool): Whether to use dynamic loss scaling. If False, fixed loss_scaling is used. If True, the loss scaling is updated dynamicly. Default is True.
"Current train mode is pure fp16, models should be paddle.nn.Layer, but receive {}.".format(
type(model)
)
)
ifisinstance(model,paddle.DataParallel):
raiseRuntimeError(
"For distributed AMP training, you should first use paddle.amp.decorate() to decotate origin model, and then call paddle.DataParallel get distributed model."
"Current train mode is pure fp16, optimizers should be paddle.optimizer.Optimizer or paddle.fluid.optimizer.Optimizer or DygraphShardingOptimizer, but receive {}.".format(
type(optimizer)
)
)
@signature_safe_contextmanager
@dygraph_only
defamp_guard(
enable=True,
custom_white_list=None,
custom_black_list=None,
level='O1',
dtype='float16',
):
"""
:api_attr: imperative
Create a context which enables auto-mixed-precision(AMP) of operators executed in dynamic graph mode.
If enabled, the input data type (float32 or float16) of each operator is decided
by autocast algorithm for better performance.
Commonly, it is used together with `GradScaler` to achieve Auto-Mixed-Precision in
imperative mode. It is used together with `decorator` to achieve Pure fp16 in imperative mode.
Args:
enable(bool, optional): Enable auto-mixed-precision or not. Default is True.
custom_white_list(set|list|tuple, optional): The custom white_list. It's the set of ops that support
fp16 calculation and are considered numerically-safe and performance-critical. These ops
will be converted to fp16.
custom_black_list(set|list|tuple, optional): The custom black_list. The set of ops that support fp16
calculation and are considered numerically-dangerous and whose effects may also be
observed in downstream ops. These ops will not be converted to fp16.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the input data type of each operator will be casted by white_list and black_list;
O2 represent Pure fp16, all operators parameters and input data will be casted to fp16, except operators in black_list, don't support fp16 kernel and batchnorm. Default is O1(amp)
dtype(str, optional): Whether to use 'float16' or 'bfloat16'. Default is 'float16'.
Examples:
.. code-block:: python
import numpy as np
import paddle
data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
with paddle.fluid.dygraph.guard():
conv2d = paddle.fluid.dygraph.Conv2D(3, 2, 3)
data = paddle.fluid.dygraph.to_variable(data)
with paddle.fluid.dygraph.amp_guard():
conv = conv2d(data)
print(conv.dtype) # FP16
with paddle.fluid.dygraph.amp_guard(enable=False):
conv = conv2d(data)
print(conv.dtype) # FP32
"""
amp_state=locals()
global_g_amp_state_
original_state=_g_amp_state_
_g_amp_state_=amp_state
# check amp_level: O0-O2
level=level.upper()
ifnot(levelin['O0','O1','O2']):
raiseValueError(
"level should be O0, O1 or O2. O0 represents fp32 train mode, O1 represents AMP train mode, O2 represents pure fp16/bf16 train mode."
)
# check amp_dtype: float16 or bfloat16
dtype=dtype.lower()
ifnot(dtypein['float16','bfloat16']):
raiseValueError("dtype should be 'float16' or 'bfloat16'.")
# check tracer
tracer=_dygraph_tracer()
ifnottracer:
raiseValueError(
"current_tracer is None, maybe it is not in imperative mode."
)
# check device_type:
# NOTE: Now, amp only support gpu for float16 and bfloat16, xpu for float16, mlu for float16, npu for float16.
# Maybe we will support cpu for bfloat16.
ifenableandnot(
tracer._expected_place.is_gpu_place()
ortracer._expected_place.is_xpu_place()
ortracer._expected_place.is_mlu_place()
ortracer._expected_place.is_npu_place()
ortracer._expected_place.is_custom_place()
):
warnings.warn(
'amp_guard can only be enabled on CUDAPlace, XPUPlace, MLUPlace, NPUPlace, and CustomPlace, current place is %s, so it makes no effect.'
"For bfloat16, amp only support NVIDIA GPU with Compute Capability 8.0 or higher and CUDA Version 11.0 or higher, current GPU is: %s, with Compute Capability: %d.%d, current CUDA Version is: %s."
Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm.
Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode.
Args:
models(Layer|list of Layer, optional): The defined models by user, models must be either a single model or a list of models. Default is None.
optimizers(Optimizer|list of Optimizer, optional): The defined optimizers by user, optimizers must be either a single optimizer or a list of optimizers. Default is None.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the decorator will do nothing;
O2 represent Pure fp16/bf16, the decorator will cast all parameters of models to FP16/BF16, except BatchNorm and LayerNorm. Default is O1(amp)
dtype(str, optional): Whether to use 'float16' or 'bfloat16'. Default is 'float16'.
master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None.
save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, bfloat16, float32, float64 or None.
The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None.
Examples:
.. code-block:: python
# required: gpu
# Demo1: single model and optimizer:
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
AmpScaler is used for Auto-Mixed-Precision training/inferring in imperative
mode. It controls the scaling of loss, helps avoiding numerical overflow.
The object of this class has seventeen methods `scale()`, `unscale_()`, `minimize()` and `get`/`set` api of parameters.
`scale()` is used to multiply the loss by a scale ratio.
`unscale_()` is used to unscale the gradients of parameters, multiplies the gradients of parameters by 1/(scale ratio)
`minimize()` is similar as `optimizer.minimize()`, performs parameters updating, and it will update the loss_scaling.
Commonly, it is used together with `amp_guard` to achieve Auto-Mixed-Precision in
imperative mode.
Args:
enable(bool, optional): Enable loss scaling or not. Default is True.
init_loss_scaling (float, optional): The initial loss scaling factor. Default is 2**15.
incr_ratio(float, optional): The multiplier to use when increasing the loss
scaling. Default is 2.0.
decr_ratio(float, optional): The less-than-one-multiplier to use when decreasing
the loss scaling. Default is 0.5.
incr_every_n_steps(int, optional): Increases loss scaling every n consecutive
steps with finite gradients. Default is 1000.
decr_every_n_nan_or_inf(int, optional): Decreases loss scaling every n
accumulated steps with nan or inf gradients. Default is 2.
use_dynamic_loss_scaling(bool, optional): Whether to use dynamic loss scaling. If False, fixed loss_scaling is used. If True, the loss scaling is updated dynamicly. Default is True.
Returns:
An AmpScaler object.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
Set the num `n` by `new_incr_every_n_steps`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
Args:
new_incr_every_n_steps(int): The new_incr_every_n_steps used to update the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients.
"""
self._incr_every_n_steps=new_incr_every_n_steps
defget_decr_every_n_nan_or_inf(self):
"""
Return the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Reurns:
int: the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Set the num `n` by `new_decr_every_n_nan_or_inf`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Args:
new_decr_every_n_nan_or_inf(int): The new_decr_every_n_nan_or_inf used to update the num `n`, `n` represent decreases loss scaling every `n` accumulated steps with nan or inf gradients.
Returns the state of the scaler as a `dict`, If this instance is not enabled, returns an empty dict.
Reurns:
A dict of scaler includes:
scale (tensor): The loss scaling factor.
incr_ratio(float): The multiplier to use when increasing the loss scaling.
decr_ratio(float): The less-than-one-multiplier to use when decreasing the loss scaling.
incr_every_n_steps(int): Increases loss scaling every n consecutive steps with finite gradients.
decr_every_n_nan_or_inf(int): Decreases loss scaling every n accumulated steps with nan or inf gradients.
incr_count(int): The number of recent consecutive unskipped steps.
decr_count(int): The number of recent consecutive skipped steps.
use_dynamic_loss_scaling(bool): Whether to use dynamic loss scaling. If False, fixed loss_scaling is used. If True, the loss scaling is updated dynamicly. Default is True.
