# 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 warnings from collections import defaultdict from enum import Enum import numpy as np from paddle import _C_ops, _legacy_C_ops from paddle.base import core from paddle.base.data_feeder import check_type from paddle.base.dygraph import to_variable from paddle.base.framework import _dygraph_tracer, dygraph_only from paddle.framework import in_dynamic_mode from .auto_cast import amp_global_state class OptimizerState(Enum): INIT = 0 UNSCALED = 1 STEPPED = 2 def _refresh_optimizer_state(): return {"state": OptimizerState.INIT} class AmpScaler: """ 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') >>> model = paddle.nn.Conv2D(3, 2, 3) >>> optimizer = paddle.optimizer.SGD( ... learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.AmpScaler(init_loss_scaling=1024) >>> data = paddle.to_tensor(data) >>> with paddle.amp.amp_guard(): ... conv = model(data) ... loss = paddle.mean(conv) ... scaled = scaler.scale(loss) ... scaled.backward() ... scaler.minimize(optimizer, scaled) """ @dygraph_only def __init__( self, enable=True, init_loss_scaling=2.0**15, incr_ratio=2.0, decr_ratio=0.5, incr_every_n_steps=1000, decr_every_n_nan_or_inf=1, use_dynamic_loss_scaling=True, ): tracer = _dygraph_tracer() if not tracer: raise ValueError( "current_tracer is None, maybe it is not in imperative mode." ) if enable and not ( tracer._expected_place.is_gpu_place() or tracer._expected_place.is_xpu_place() or tracer._expected_place.is_custom_place() ): warnings.warn( 'AmpScaler can only be enabled on CUDAPlace, XPUPlace and CustomPlace, current place is %s, so it makes no effect.' % tracer._expected_place ) enable = False self._enable = enable if self._enable: assert incr_ratio > 1.0, "The incr_ratio must be > 1.0." assert decr_ratio < 1.0, "The decr_ratio must be < 1.0." self._init_loss_scaling = init_loss_scaling self._incr_ratio = incr_ratio self._decr_ratio = decr_ratio self._incr_every_n_steps = incr_every_n_steps self._decr_every_n_nan_or_inf = decr_every_n_nan_or_inf self._incr_count = 0 self._decr_count = 0 self._use_dynamic_loss_scaling = use_dynamic_loss_scaling self._found_inf = to_variable(np.array([0]).astype(np.bool_)) self._temp_found_inf_value_false = to_variable( np.array([0]).astype(np.bool_) ) self._temp_found_inf_fp16 = to_variable( np.array([0]).astype(np.bool_) ) self._temp_found_inf_bf16 = to_variable( np.array([0]).astype(np.bool_) ) self._temp_found_inf_fp32 = to_variable( np.array([0]).astype(np.bool_) ) self._scale = to_variable( np.array([self._init_loss_scaling]).astype(np.float32) ) self._cache_founf_inf = None self._optimizer_states = defaultdict(_refresh_optimizer_state) def scale(self, var): """ Multiplies a Tensor by the scale factor and returns scaled outputs. If this instance of :class:`AmpScaler` is not enabled, output are returned unmodified. Args: var (Tensor): The Tensor to scale. Returns: The scaled Tensor or original Tensor. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32') >>> model = paddle.nn.Conv2D(3, 2, 3) >>> optimizer = paddle.optimizer.SGD( ... learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.AmpScaler(init_loss_scaling=1024) >>> data = paddle.to_tensor(data) >>> with paddle.amp.amp_guard(): ... conv = model(data) ... loss = paddle.mean(conv) ... scaled = scaler.scale(loss) ... scaled.backward() ... scaler.minimize(optimizer, scaled) """ check_type(var, "var", core.eager.Tensor, 'AmpScaler.scale()') if ( self._enable and amp_global_state().amp_dtype != 'float16' and self._use_dynamic_loss_scaling ): self._enable = False self._use_dynamic_loss_scaling = False warnings.warn( 'It is not recommended to use dynamic loss scaling for %s, so GradScaler is disable by default.' % (amp_global_state().amp_dtype) ) if not self._enable: return var return var * self._scale def minimize(self, optimizer, *args, **kwargs): """ This function is similar as `Optimizer.minimize()`, which performs parameters updating. If the scaled gradients of parameters contains NAN or INF, the parameters updating is skipped. Otherwise, if `unscale_()` has not been called, it first unscales the scaled gradients of parameters, then updates the parameters. Finally, the loss scaling ratio is updated. Args: optimizer(Optimizer): The optimizer used to update parameters. args: Arguments, which will be forward to `optimizer.minimize()`. kwargs: Keyword arguments, which will be forward to `Optimizer.minimize()`. Examples: .. code-block:: python >>> import numpy as np >>> import paddle >>> data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32') >>> model = paddle.nn.Conv2D(3, 2, 3) >>> optimizer = paddle.optimizer.SGD( ... learning_rate=0.01, ... parameters=model.parameters() ... ) >>> scaler = paddle.amp.AmpScaler(init_loss_scaling=1024) >>> data = paddle.to_tensor(data) >>> with paddle.amp.amp_guard(): ... conv = model(data) ... loss = paddle.mean(conv) ... scaled = scaler.scale(loss) ... scaled.backward() ... scaler.minimize(optimizer, scaled) """ if not self._enable: return optimizer.minimize(*args, **kwargs) optimizer_state = self._optimizer_states[id(optimizer)] # unscale the grad if optimizer_state["state"] is OptimizerState.INIT: self._unscale(optimizer) optimize_ops, params_grads = (None, None) if hasattr(optimizer, "_set_auxiliary_var"): optimizer._set_auxiliary_var('found_inf', self._found_inf) optimize_ops, params_grads = optimizer.minimize(*args, **kwargs) self._cache_founf_inf = optimizer._get_auxiliary_var('found_inf') else: if self._found_inf: self._cache_founf_inf = True else: optimize_ops, params_grads = optimizer.minimize(*args, **kwargs) self._cache_founf_inf = False if self._use_dynamic_loss_scaling: # uopdate the scale self._update() self._optimizer_states = defaultdict(_refresh_optimizer_state) return optimize_ops, params_grads def _unscale(self, optimizer): """ Unscale the gradients of parameters, multiplies the gradients of parameters by 1/(loss scaling ratio). If this instance of :class:`GradScaler` is not enabled, output are returned unmodified. Args: optimizer(Optimizer): The optimizer used to update parameters. Returns: The unscaled parameters or original parameters. """ if not self._enable: return optimizer_state = self._optimizer_states[id(optimizer)] if optimizer_state["state"] is OptimizerState.UNSCALED: raise RuntimeError( "unscale_() has already been called on this optimizer since the last update()." ) elif optimizer_state["state"] is OptimizerState.STEPPED: raise RuntimeError("unscale_() is being called after step().") if getattr(optimizer, '_param_groups', None) and isinstance( optimizer._param_groups[0], dict ): param_grads = [] param_grads_fp16 = [] param_grads_bf16 = [] param_grads_fp32 = [] for group in optimizer._param_groups: for param in group['params']: if param._grad_ivar() is not None: param_grads.append(param._grad_ivar()) if ( param._grad_ivar().dtype == core.VarDesc.VarType.FP16 ): param_grads_fp16.append(param._grad_ivar()) elif ( param._grad_ivar().dtype == core.VarDesc.VarType.BF16 ): param_grads_bf16.append(param._grad_ivar()) else: param_grads_fp32.append(param._grad_ivar()) else: if in_dynamic_mode(): # It is very time-consuming to call c++ functions in a loop on the python side. # We put this part of the code on the c++ side to improve the speed in eager mode. ( param_grads_fp16, param_grads_bf16, param_grads_fp32, ) = core.eager.get_grads_lists(optimizer._parameter_list) else: # Keep the original code to support legacy mode. # Delete the else branch when the legacy mode exits. param_grads = [ param._grad_ivar() for param in optimizer._parameter_list if param._grad_ivar() is not None ] param_grads_fp16 = [ param for param in param_grads if param.dtype == core.VarDesc.VarType.FP16 ] param_grads_bf16 = [ param for param in param_grads if param.dtype == core.VarDesc.VarType.BF16 ] param_grads_fp32 = [ param for param in param_grads if param.dtype == core.VarDesc.VarType.FP32 ] self._found_inf = self._temp_found_inf_value_false if len(param_grads_fp16): _legacy_C_ops.check_finite_and_unscale( param_grads_fp16, self._scale, param_grads_fp16, self._temp_found_inf_fp16, ) self._found_inf = _C_ops.bitwise_or( self._found_inf, self._temp_found_inf_fp16 ) if len(param_grads_bf16): _legacy_C_ops.check_finite_and_unscale( param_grads_bf16, self._scale, param_grads_bf16, self._temp_found_inf_bf16, ) self._found_inf = _C_ops.bitwise_or( self._found_inf, self._temp_found_inf_bf16 ) if len(param_grads_fp32): _legacy_C_ops.check_finite_and_unscale( param_grads_fp32, self._scale, param_grads_fp32, self._temp_found_inf_fp32, ) self._found_inf = _C_ops.bitwise_or( self._found_inf, self._temp_found_inf_fp32 ) optimizer_state["state"] = OptimizerState.UNSCALED def _update(self): """ Updates the loss_scaling. """ if not self._enable: return if self._cache_founf_inf: self._incr_count = 0 self._decr_count = self._decr_count + 1 if self._decr_count == self._decr_every_n_nan_or_inf: print( 'Found inf or nan, current scale is: {}, decrease to: {}*{}'.format( float(self._scale), float(self._scale), float(self._decr_ratio), ) ) self._scale = self._scale * self._decr_ratio self._decr_count = 0 else: self._decr_count = 0 self._incr_count = self._incr_count + 1 if self._incr_count == self._incr_every_n_steps: self._scale = self._scale * self._incr_ratio self._incr_count = 0 return def is_enable(self): """ Enable loss scaling or not. Returns: bool: enable loss scaling return True else return False. """ return self._enable def is_use_dynamic_loss_scaling(self): """ Whether to use dynamic loss scaling. Returns: bool: if fixed loss_scaling is used return False, if the loss scaling is updated dynamicly return true. """ return self._use_dynamic_loss_scaling def get_init_loss_scaling(self): """ Return the initial loss scaling factor. Reurns: float: the initial loss scaling factor. """ return self._init_loss_scaling def set_init_loss_scaling(self, new_init_loss_scaling): """ Set the initial loss scaling factor by `new_init_loss_scaling`. Args: new_init_loss_scaling(int): The new_init_loss_scaling used to update initial loss scaling factor.s """ self._init_loss_scaling = new_init_loss_scaling self._scale = to_variable( np.array([self._init_loss_scaling]).astype(np.float32) ) def get_incr_ratio(self): """ Return the multiplier to use when increasing the loss scaling. Reurns: float: the multiplier to use when increasing the loss scaling. """ return self._incr_ratio def set_incr_ratio(self, new_incr_ratio): """ Set the multiplier to use when increasing the loss scaling by `new_incr_ratio`, `new_incr_ratio` should > 1.0. Args: new_incr_ratio(float): The new_incr_ratio used to update the multiplier to use when increasing the loss scaling. """ assert new_incr_ratio > 1.0, "The new_incr_ratio must be > 1.0." self._incr_ratio = new_incr_ratio def get_decr_ratio(self): """ Get the less-than-one-multiplier to use when decreasing the loss scaling. Reurns: float: the less-than-one-multiplier to use when decreasing the loss scaling. """ return self._decr_ratio def set_decr_ratio(self, new_decr_ratio): """ Set the less-than-one-multiplier to use when decreasing the loss scaling by `new_incr_ratio`, `new_decr_ratio` should < 1.0. Args: new_decr_ratio(float): The new_decr_ratio used to update the less-than-one-multiplier to use when decreasing the loss scaling. """ assert new_decr_ratio < 1.0, "The new_decr_ratio must be < 1.0." self._decr_ratio = new_decr_ratio def get_incr_every_n_steps(self): """ Return the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients. Reurns: int: the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients. """ return self._incr_every_n_steps def set_incr_every_n_steps(self, new_incr_every_n_steps): """ 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 def get_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. """ return self._decr_every_n_nan_or_inf def set_decr_every_n_nan_or_inf(self, new_decr_every_n_nan_or_inf): """ 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. """ self._decr_every_n_nan_or_inf = new_decr_every_n_nan_or_inf def state_dict(self): """ 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. """ return ( { "scale": self._scale.numpy(), "incr_ratio": self._incr_ratio, "decr_ratio": self._decr_ratio, "incr_every_n_steps": self._incr_every_n_steps, "decr_every_n_nan_or_inf": self._decr_every_n_nan_or_inf, "incr_count": self._incr_count, "decr_count": self._decr_count, "use_dynamic_loss_scaling": self._use_dynamic_loss_scaling, } if self._enable else {} ) def load_state_dict(self, state_dict): """ Loads the scaler state. Args: state_dict(dict): scaler state. Should be an object returned from a call to `AmpScaler.state_dict()`. """ if not self._enable: return if len(state_dict) == 0: raise RuntimeError( "The input state dict is empty, possibly because it was saved " "from a disabled instance of GradScaler." ) self._init_loss_scaling = state_dict["scale"][0] self._scale = to_variable( np.array([self._init_loss_scaling]).astype(np.float32) ) self._incr_ratio = state_dict["incr_ratio"] self._decr_ratio = state_dict["decr_ratio"] self._incr_every_n_steps = state_dict["incr_every_n_steps"] self._decr_every_n_nan_or_inf = state_dict["decr_every_n_nan_or_inf"] self._incr_count = state_dict["incr_count"] self._decr_count = state_dict["decr_count"] self._use_dynamic_loss_scaling = state_dict["use_dynamic_loss_scaling"] class GradScaler(AmpScaler): """ GradScaler is used for Auto-Mixed-Precision training in dynamic graph mode. It controls the scaling of loss, helps avoiding numerical overflow. The object of this class has nineteen methods `scale()`, `unscale_()`, `minimize()`, `step()`, `update()` 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, it equal to `step()` + `update()`. `step()` is similar as `optimizer.step()`, which performs parameters updating. `update` is used to update the loss_scaling. Commonly, it is used together with `paddle.amp.auto_cast` to achieve Auto-Mixed-Precision in dynamic graph 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 65536.0. 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 2000. decr_every_n_nan_or_inf(int, optional): Decreases loss scaling every n accumulated steps with nan or inf gradients. Default is 1. 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 GradScaler object. Examples: .. code-block:: python >>> import paddle >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.minimize(optimizer, scaled) # update parameters >>> optimizer.clear_grad() """ def __init__( self, enable=True, init_loss_scaling=2.0**16, incr_ratio=2.0, decr_ratio=0.5, incr_every_n_steps=2000, decr_every_n_nan_or_inf=1, use_dynamic_loss_scaling=True, ): super().__init__( enable, init_loss_scaling, incr_ratio, decr_ratio, incr_every_n_steps, decr_every_n_nan_or_inf, use_dynamic_loss_scaling, ) def scale(self, var): """ Multiplies a Tensor by the scale factor and returns scaled outputs. If this instance of :class:`GradScaler` is not enabled, output are returned unmodified. Args: var (Tensor): The tensor to scale. Returns: The scaled tensor or original tensor. Examples: .. code-block:: python >>> import paddle >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.minimize(optimizer, scaled) # update parameters >>> optimizer.clear_grad() """ return super().scale(var) def minimize(self, optimizer, *args, **kwargs): """ This function is similar as `optimizer.minimize()`, which performs parameters updating. If the scaled gradients of parameters contains NAN or INF, the parameters updating is skipped. Otherwise, if `unscale_()` has not been called, it first unscales the scaled gradients of parameters, then updates the parameters. Finally, the loss scaling ratio is updated. Args: optimizer(Optimizer): The optimizer used to update parameters. args: Arguments, which will be forward to `optimizer.minimize()`. kwargs: Keyword arguments, which will be forward to `optimizer.minimize()`. Examples: .. code-block:: python >>> import paddle >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.minimize(optimizer, scaled) # update parameters >>> optimizer.clear_grad() """ return super().minimize(optimizer, *args, **kwargs) def step(self, optimizer): """ This function is similar as `optimizer.step()`, which performs parameters updating. If the scaled gradients of parameters contains NAN or INF, the parameters updating is skipped. Otherwise, if `unscale_()` has not been called, it first unscales the scaled gradients of parameters, then updates the parameters. Args: optimizer(Optimizer): The optimizer used to update parameters. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.step(optimizer) # update parameters >>> scaler.update() # update the loss scaling ratio >>> optimizer.clear_grad() """ if not self._enable: return optimizer.step() optimizer_state = self._optimizer_states[id(optimizer)] if optimizer_state["state"] is OptimizerState.STEPPED: raise RuntimeError( "step() has already been called since the last update()." ) # unscale the grad if optimizer_state["state"] is OptimizerState.INIT: self._unscale(optimizer) if hasattr(optimizer, "_set_auxiliary_var"): optimizer._set_auxiliary_var('found_inf', self._found_inf) optimizer.step() self._cache_founf_inf = optimizer._get_auxiliary_var('found_inf') else: if self._found_inf: self._cache_founf_inf = True else: optimizer.step() self._cache_founf_inf = False optimizer_state["state"] = OptimizerState.STEPPED if not self._use_dynamic_loss_scaling: self._optimizer_states = defaultdict(_refresh_optimizer_state) def update(self): """ Updates the loss_scaling. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.step(optimizer) # update parameters >>> scaler.update() # update the loss scaling ratio >>> optimizer.clear_grad() """ if not self._enable: return if self._use_dynamic_loss_scaling: self._update() self._optimizer_states = defaultdict(_refresh_optimizer_state) return def unscale_(self, optimizer): """ Unscale the gradients of parameters, multiplies the gradients of parameters by 1/(loss scaling ratio). If this instance of :class:`GradScaler` is not enabled, output are returned unmodified. Args: optimizer(Optimizer): The optimizer used to update parameters. Returns: The unscaled parameters or original parameters. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> model = paddle.nn.Conv2D(3, 2, 3, bias_attr=True) >>> optimizer = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) >>> scaler = paddle.amp.GradScaler(init_loss_scaling=1024) >>> data = paddle.rand([10, 3, 32, 32]) >>> with paddle.amp.auto_cast(): ... conv = model(data) ... loss = paddle.mean(conv) >>> scaled = scaler.scale(loss) # scale the loss >>> scaled.backward() # do backward >>> scaler.unscale_(optimizer) # unscale the parameter >>> scaler.step(optimizer) >>> scaler.update() >>> optimizer.clear_grad() """ return super()._unscale(optimizer) def is_enable(self): """ Enable loss scaling or not. Returns: bool: enable loss scaling return True else return False. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> enable = scaler.is_enable() >>> print(enable) True """ return super().is_enable() def is_use_dynamic_loss_scaling(self): """ Whether to use dynamic loss scaling. Returns: bool: if fixed loss_scaling is used return False, if the loss scaling is updated dynamicly return true. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> use_dynamic_loss_scaling = scaler.is_use_dynamic_loss_scaling() >>> print(use_dynamic_loss_scaling) True """ return super().is_use_dynamic_loss_scaling() def get_init_loss_scaling(self): """ Return the initial loss scaling factor. Reurns: float: the initial loss scaling factor. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> init_loss_scaling = scaler.get_init_loss_scaling() >>> print(init_loss_scaling) 1024 """ return super().get_init_loss_scaling() def set_init_loss_scaling(self, new_init_loss_scaling): """ Set the initial loss scaling factor by `new_init_loss_scaling`. Args: new_init_loss_scaling(float): The new_init_loss_scaling used to update initial loss scaling factor. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> print(scaler.get_init_loss_scaling()) 1024 >>> new_init_loss_scaling = 1000 >>> scaler.set_init_loss_scaling(new_init_loss_scaling) >>> print(scaler.get_init_loss_scaling()) 1000 """ super().set_init_loss_scaling(new_init_loss_scaling) def get_incr_ratio(self): """ Return the multiplier to use when increasing the loss scaling. Reurns: float: the multiplier to use when increasing the loss scaling. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> incr_ratio = scaler.get_incr_ratio() >>> print(incr_ratio) 2.0 """ return super().get_incr_ratio() def set_incr_ratio(self, new_incr_ratio): """ Set the multiplier to use when increasing the loss scaling by `new_incr_ratio`, `new_incr_ratio` should > 1.0. Args: new_incr_ratio(float): The new_incr_ratio used to update the multiplier to use when increasing the loss scaling. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> print(scaler.get_incr_ratio()) 2.0 >>> new_incr_ratio = 3.0 >>> scaler.set_incr_ratio(new_incr_ratio) >>> print(scaler.get_incr_ratio()) 3.0 """ super().set_incr_ratio(new_incr_ratio) def get_decr_ratio(self): """ Get the less-than-one-multiplier to use when decreasing the loss scaling. Reurns: float: the less-than-one-multiplier to use when decreasing the loss scaling. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> decr_ratio = scaler.get_decr_ratio() >>> print(decr_ratio) 0.5 """ return super().get_decr_ratio() def set_decr_ratio(self, new_decr_ratio): """ Set the less-than-one-multiplier to use when decreasing the loss scaling by `new_incr_ratio`, `new_decr_ratio` should < 1.0. Args: new_decr_ratio(float): The new_decr_ratio used to update the less-than-one-multiplier to use when decreasing the loss scaling. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> print(scaler.get_decr_ratio()) 0.5 >>> new_decr_ratio = 0.1 >>> scaler.set_decr_ratio(new_decr_ratio) >>> print(scaler.get_decr_ratio()) 0.1 """ super().set_decr_ratio(new_decr_ratio) def get_incr_every_n_steps(self): """ Return the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients. Reurns: int: the num `n`, `n` represent increases loss scaling every `n` consecutive steps with finite gradients. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> incr_every_n_steps = scaler.get_incr_every_n_steps() >>> print(incr_every_n_steps) 1000 """ return super().get_incr_every_n_steps() def set_incr_every_n_steps(self, new_incr_every_n_steps): """ 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. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> print(scaler.get_incr_every_n_steps()) 1000 >>> new_incr_every_n_steps = 2000 >>> scaler.set_incr_every_n_steps(new_incr_every_n_steps) >>> print(scaler.get_incr_every_n_steps()) 2000 """ super().set_incr_every_n_steps(new_incr_every_n_steps) def get_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. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> decr_every_n_nan_or_inf = scaler.get_decr_every_n_nan_or_inf() >>> print(decr_every_n_nan_or_inf) 2 """ return super().get_decr_every_n_nan_or_inf() def set_decr_every_n_nan_or_inf(self, new_decr_every_n_nan_or_inf): """ 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. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> print(scaler.get_decr_every_n_nan_or_inf()) 2 >>> new_decr_every_n_nan_or_inf = 3 >>> scaler.set_decr_every_n_nan_or_inf(new_decr_every_n_nan_or_inf) >>> print(scaler.get_decr_every_n_nan_or_inf()) 3 """ super().set_decr_every_n_nan_or_inf(new_decr_every_n_nan_or_inf) def state_dict(self): """ Returns the state of the scaler as a `dict`, If this instance is not enabled, returns an empty dict. Returns: 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. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> scaler_state = scaler.state_dict() """ return super().state_dict() def load_state_dict(self, state_dict): """ Loads the scaler state. Args: state_dict(dict): scaler state. Should be an object returned from a call to `GradScaler.state_dict()`. Examples: .. code-block:: python >>> # doctest: +REQUIRES(env:GPU, env:XPU) >>> import paddle >>> scaler = paddle.amp.GradScaler( ... enable=True, ... init_loss_scaling=1024, ... incr_ratio=2.0, ... decr_ratio=0.5, ... incr_every_n_steps=1000, ... decr_every_n_nan_or_inf=2, ... use_dynamic_loss_scaling=True ... ) >>> scaler_state = scaler.state_dict() >>> scaler.load_state_dict(scaler_state) """ super().load_state_dict(state_dict)