| from typing import List, Optional |
| |
| import torch |
| from torch import Tensor |
| from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _stack_if_compiling, |
| _dispatch_sqrt, _default_to_fused_or_foreach, _capturable_doc, |
| _differentiable_doc, _foreach_doc, _maximize_doc) |
| from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype |
| |
| __all__ = ['Adam', 'adam'] |
| |
| |
| class Adam(Optimizer): |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, |
| weight_decay=0, amsgrad=False, *, foreach: Optional[bool] = None, |
| maximize: bool = False, capturable: bool = False, |
| differentiable: bool = False, fused: Optional[bool] = None): |
| if not 0.0 <= lr: |
| raise ValueError("Invalid learning rate: {}".format(lr)) |
| if not 0.0 <= eps: |
| raise ValueError("Invalid epsilon value: {}".format(eps)) |
| if not 0.0 <= betas[0] < 1.0: |
| raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) |
| if not 0.0 <= betas[1] < 1.0: |
| raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) |
| if not 0.0 <= weight_decay: |
| raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) |
| |
| defaults = dict(lr=lr, betas=betas, eps=eps, |
| weight_decay=weight_decay, amsgrad=amsgrad, |
| maximize=maximize, foreach=foreach, capturable=capturable, |
| differentiable=differentiable, fused=fused) |
| super(Adam, self).__init__(params, defaults) |
| |
| if fused: |
| if differentiable: |
| raise RuntimeError("`fused` does not support `differentiable`") |
| self._step_supports_amp_scaling = True |
| # TODO(crcrpar): [low prec params & their higher prec copy] |
| # Suppor AMP with FP16/BF16 model params which would need |
| # higher prec copy of params to do update math in higher prec to |
| # alleviate the loss of information. |
| if not all( |
| p.is_cuda and torch.is_floating_point(p) |
| for pg in self.param_groups for p in pg['params'] |
| ): |
| raise RuntimeError("`fused=True` requires all the params to be CUDA, floating point Tensor") |
| if foreach: |
| raise RuntimeError("`fused` and `foreach` cannot be `True` together.") |
| |
| def __setstate__(self, state): |
| super().__setstate__(state) |
| for group in self.param_groups: |
| group.setdefault('amsgrad', False) |
| group.setdefault('maximize', False) |
| group.setdefault('foreach', None) |
| group.setdefault('capturable', False) |
| group.setdefault('differentiable', False) |
| group.setdefault('fused', None) |
| state_values = list(self.state.values()) |
| step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step']) |
| if not step_is_tensor: |
| for s in state_values: |
| s['step'] = torch.tensor(float(s['step'])) |
| |
| def _init_group( |
| self, |
| group, |
| params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps |
| ): |
| for p in group['params']: |
| if p.grad is not None: |
| params_with_grad.append(p) |
| if p.grad.is_sparse: |
| raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') |
| grads.append(p.grad) |
| |
| state = self.state[p] |
| # Lazy state initialization |
| if len(state) == 0: |
| state['step'] = ( |
| torch.zeros((1,), dtype=torch.float, device=p.device) |
| if self.defaults['capturable'] or self.defaults['fused'] |
| else torch.tensor(0.) |
| ) |
| # Exponential moving average of gradient values |
| state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| # Exponential moving average of squared gradient values |
| state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| if group['amsgrad']: |
| # Maintains max of all exp. moving avg. of sq. grad. values |
| state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| |
| exp_avgs.append(state['exp_avg']) |
| exp_avg_sqs.append(state['exp_avg_sq']) |
| |
| if group['amsgrad']: |
| max_exp_avg_sqs.append(state['max_exp_avg_sq']) |
| if group['differentiable'] and state['step'].requires_grad: |
| raise RuntimeError('`requires_grad` is not supported for `step` in differentiable mode') |
| state_steps.append(state['step']) |
| |
| @_use_grad_for_differentiable |
| def step(self, closure=None): |
| """Performs a single optimization step. |
| |
| Args: |
| closure (Callable, optional): A closure that reevaluates the model |
| and returns the loss. |
| grad_scaler (:class:`torch.cuda.amp.GradScaler`, optional): A GradScaler which is |
| supplied from ``grad_scaler.step(optimizer)``. |
| """ |
| self._cuda_graph_capture_health_check() |
| |
| loss = None |
| if closure is not None: |
| with torch.enable_grad(): |
| loss = closure() |
| |
| for group in self.param_groups: |
| params_with_grad = [] |
| grads = [] |
| exp_avgs = [] |
| exp_avg_sqs = [] |
| max_exp_avg_sqs = [] |
| state_steps = [] |
| beta1, beta2 = group['betas'] |
| |
| self._init_group( |
| group, |
| params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps) |
| |
| adam(params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps, |
| amsgrad=group['amsgrad'], |
| beta1=beta1, |
| beta2=beta2, |
| lr=group['lr'], |
| weight_decay=group['weight_decay'], |
| eps=group['eps'], |
| maximize=group['maximize'], |
| foreach=group['foreach'], |
| capturable=group['capturable'], |
| differentiable=group['differentiable'], |
| fused=group['fused'], |
| grad_scale=getattr(self, "grad_scale", None), |
| found_inf=getattr(self, "found_inf", None)) |
| |
| return loss |
| |
| |
| Adam.__doc__ = r"""Implements Adam algorithm. |
| |
| .. math:: |
| \begin{aligned} |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2 |
| \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)} \\ |
| &\hspace{13mm} \lambda \text{ (weight decay)}, \: \textit{amsgrad}, |
| \:\textit{maximize} \\ |
| &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, |
| v_0\leftarrow 0 \text{ (second moment)},\: \widehat{v_0}^{max}\leftarrow 0\\[-1.ex] |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ |
| |
| &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ |
| &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ |
| &\hspace{5mm}\textbf{else} \\ |
| &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ |
| &\hspace{5mm}\textbf{if} \: \lambda \neq 0 \\ |
| &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ |
| &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ |
| &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ |
| &\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ |
| &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ |
| &\hspace{5mm}\textbf{if} \: amsgrad \\ |
| &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max}, |
| \widehat{v_t}) \\ |
| &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ |
| \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ |
| &\hspace{5mm}\textbf{else} \\ |
| &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ |
| \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ |
| &\rule{110mm}{0.4pt} \\[-1.ex] |
| &\bf{return} \: \theta_t \\[-1.ex] |
| &\rule{110mm}{0.4pt} \\[-1.ex] |
| \end{aligned} |
| |
| For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_. |
| """ + r""" |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, optional): learning rate (default: 1e-3) |
| betas (Tuple[float, float], optional): coefficients used for computing |
| running averages of gradient and its square (default: (0.9, 0.999)) |
| eps (float, optional): term added to the denominator to improve |
| numerical stability (default: 1e-8) |
| weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
| amsgrad (bool, optional): whether to use the AMSGrad variant of this |
| algorithm from the paper `On the Convergence of Adam and Beyond`_ |
| (default: False) |
| {foreach} |
| {maximize} |
| {capturable} |
| {differentiable} |
| fused (bool, optional): whether the fused implementation (CUDA only) is used. |
| Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16` |
| are supported. Since the fused implementation is usually significantly faster than |
| the for-loop implementation, we try to use it whenever possible (all parameters |
| are on CUDA and are of a supported type). Else, we attempt to use the foreach |
| implementation and lastly fall back to the for-loop implementation. (default: None) |
| |
| .. note:: The foreach and fused implementations are typically faster than the for-loop, |
| single-tensor implementation, so we will try to default to them IF the user has |
| not specified either flag (i.e., when foreach = fused = None). For example, if |
| the user specifies True for foreach but nothing for fused, we will run the foreach |
| implementation. If the user specifies False for fused but nothing for foreach, we will |
| run the for-loop implementation. If the user specifies True for both foreach and |
| fused, we will prioritize fused over foreach. We attempt to use the fastest, so the |
| hierarchy goes fused -> foreach -> for-loop. |
| .. _Adam\: A Method for Stochastic Optimization: |
| https://arxiv.org/abs/1412.6980 |
| .. _On the Convergence of Adam and Beyond: |
| https://openreview.net/forum?id=ryQu7f-RZ |
| |
| """.format(foreach=_foreach_doc, maximize=_maximize_doc, capturable=_capturable_doc, |
| differentiable=_differentiable_doc) |
| |
| |
| def adam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 |
| # setting this as kwarg for now as functional API is compiled by torch/distributed/optim |
| foreach: Optional[bool] = None, |
| capturable: bool = False, |
| differentiable: bool = False, |
| fused: Optional[bool] = None, |
| grad_scale: Optional[Tensor] = None, |
| found_inf: Optional[Tensor] = None, |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool): |
| r"""Functional API that performs Adam algorithm computation. |
| See :class:`~torch.optim.Adam` for details. |
| """ |
| |
| if fused is None and foreach is None: |
| fused, foreach = _default_to_fused_or_foreach( |
| [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps], |
| differentiable, has_fused=True) |
| if fused is None: |
| fused = False |
| if foreach is None: |
| foreach = False |
| |
| if not all(isinstance(t, torch.Tensor) for t in state_steps): |
| raise RuntimeError("API has changed, `state_steps` argument must contain a list of singleton tensors") |
| |
| if foreach and torch.jit.is_scripting(): |
| raise RuntimeError('torch.jit.script not supported with foreach optimizers') |
| |
| if fused and not torch.jit.is_scripting(): |
| func = _fused_adam |
| elif foreach and not torch.jit.is_scripting(): |
| func = _multi_tensor_adam |
| else: |
| func = _single_tensor_adam |
| |
| func(params, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps, |
| amsgrad=amsgrad, |
| beta1=beta1, |
| beta2=beta2, |
| lr=lr, |
| weight_decay=weight_decay, |
| eps=eps, |
| maximize=maximize, |
| capturable=capturable, |
| differentiable=differentiable, |
| grad_scale=grad_scale, |
| found_inf=found_inf) |
| |
| |
| def _single_tensor_adam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| grad_scale: Optional[Tensor], |
| found_inf: Optional[Tensor], |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool): |
| |
| assert grad_scale is None and found_inf is None |
| |
| for i, param in enumerate(params): |
| |
| grad = grads[i] if not maximize else -grads[i] |
| exp_avg = exp_avgs[i] |
| exp_avg_sq = exp_avg_sqs[i] |
| step_t = state_steps[i] |
| |
| if capturable: |
| assert param.is_cuda and step_t.is_cuda, "If capturable=True, params and state_steps must be CUDA tensors." |
| |
| # update step |
| step_t += 1 |
| |
| if weight_decay != 0: |
| grad = grad.add(param, alpha=weight_decay) |
| |
| if torch.is_complex(param): |
| grad = torch.view_as_real(grad) |
| exp_avg = torch.view_as_real(exp_avg) |
| exp_avg_sq = torch.view_as_real(exp_avg_sq) |
| param = torch.view_as_real(param) |
| |
| # Decay the first and second moment running average coefficient |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2) |
| |
| if capturable or differentiable: |
| step = step_t |
| |
| # 1 - beta1 ** step can't be captured in a CUDA graph, even if step is a CUDA tensor |
| # (incurs "RuntimeError: CUDA error: operation not permitted when stream is capturing") |
| bias_correction1 = 1 - torch.pow(beta1, step) |
| bias_correction2 = 1 - torch.pow(beta2, step) |
| |
| step_size = lr / bias_correction1 |
| step_size_neg = step_size.neg() |
| |
| bias_correction2_sqrt = bias_correction2.sqrt() |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| if differentiable: |
| max_exp_avg_sqs_i = max_exp_avg_sqs[i].clone() |
| else: |
| max_exp_avg_sqs_i = max_exp_avg_sqs[i] |
| max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sqs_i, exp_avg_sq)) |
| # Uses the max. for normalizing running avg. of gradient |
| # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write |
| # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor) |
| denom = (max_exp_avg_sqs[i].sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg) |
| else: |
| denom = (exp_avg_sq.sqrt() / (bias_correction2_sqrt * step_size_neg)).add_(eps / step_size_neg) |
| |
| param.addcdiv_(exp_avg, denom) |
| else: |
| step = _get_value(step_t) |
| |
| bias_correction1 = 1 - beta1 ** step |
| bias_correction2 = 1 - beta2 ** step |
| |
| step_size = lr / bias_correction1 |
| |
| bias_correction2_sqrt = _dispatch_sqrt(bias_correction2) |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i]) |
| # Use the max. for normalizing running avg. of gradient |
| denom = (max_exp_avg_sqs[i].sqrt() / bias_correction2_sqrt).add_(eps) |
| else: |
| denom = (exp_avg_sq.sqrt() / bias_correction2_sqrt).add_(eps) |
| |
| param.addcdiv_(exp_avg, denom, value=-step_size) |
| |
| |
| def _multi_tensor_adam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| grad_scale: Optional[Tensor], |
| found_inf: Optional[Tensor], |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool): |
| if len(params) == 0: |
| return |
| |
| if capturable: |
| assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \ |
| "If capturable=True, params and state_steps must be CUDA tensors." |
| |
| assert grad_scale is None and found_inf is None |
| |
| assert not differentiable, "_foreach ops don't support autograd" |
| |
| grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]) |
| for (device_params, device_grads, device_exp_avgs, device_exp_avg_sqs, |
| device_max_exp_avg_sqs, device_state_steps) in grouped_tensors.values(): |
| |
| if maximize: |
| device_grads = torch._foreach_neg(tuple(device_grads)) # type: ignore[assignment] |
| |
| # Handle complex parameters |
| device_grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_grads] |
| device_exp_avgs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_exp_avgs] |
| device_exp_avg_sqs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_exp_avg_sqs] |
| params_ = [torch.view_as_real(x) if torch.is_complex(x) else x for x in device_params] |
| |
| # update steps |
| torch._foreach_add_(device_state_steps, 1) |
| |
| if weight_decay != 0: |
| device_grads = torch._foreach_add(device_grads, device_params, alpha=weight_decay) |
| |
| # Decay the first and second moment running average coefficient |
| torch._foreach_mul_(device_exp_avgs, beta1) |
| torch._foreach_add_(device_exp_avgs, device_grads, alpha=1 - beta1) |
| |
| torch._foreach_mul_(device_exp_avg_sqs, beta2) |
| torch._foreach_addcmul_(device_exp_avg_sqs, device_grads, device_grads, 1 - beta2) |
| |
| if capturable: |
| # TODO: use foreach_pow if/when foreach_pow is added |
| bias_correction1 = [torch.pow(beta1, step) for step in device_state_steps] |
| bias_correction2 = [torch.pow(beta2, step) for step in device_state_steps] |
| # foreach_sub doesn't allow a scalar as the first arg |
| torch._foreach_sub_(bias_correction1, 1) |
| torch._foreach_sub_(bias_correction2, 1) |
| torch._foreach_neg_(bias_correction1) |
| torch._foreach_neg_(bias_correction2) |
| |
| # foreach_div doesn't allow a scalar as the first arg |
| step_size = torch._foreach_div(bias_correction1, lr) |
| torch._foreach_reciprocal_(step_size) |
| torch._foreach_neg_(step_size) |
| |
| bias_correction2_sqrt = torch._foreach_sqrt(bias_correction2) |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs) # type: ignore[assignment] |
| |
| # Use the max. for normalizing running avg. of gradient |
| max_exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs) |
| # Folds in (admittedly ugly) 1-elem step_size math here to avoid extra param-set-sized read+write |
| # (can't fold it into addcdiv_ below because addcdiv_ requires value is a Number, not a Tensor) |
| torch._foreach_div_(max_exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)) |
| eps_over_step_size = torch._foreach_div(step_size, eps) |
| torch._foreach_reciprocal_(eps_over_step_size) |
| denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps_over_step_size) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs) |
| torch._foreach_div_(exp_avg_sq_sqrt, torch._foreach_mul(bias_correction2_sqrt, step_size)) |
| eps_over_step_size = torch._foreach_div(step_size, eps) |
| torch._foreach_reciprocal_(eps_over_step_size) |
| denom = torch._foreach_add(exp_avg_sq_sqrt, eps_over_step_size) |
| |
| torch._foreach_addcdiv_(params_, device_exp_avgs, denom) |
| else: |
| bias_correction1 = [1 - beta1 ** _get_value(step) for step in device_state_steps] |
| bias_correction2 = [1 - beta2 ** _get_value(step) for step in device_state_steps] |
| |
| step_size = _stack_if_compiling([(lr / bc) * -1 for bc in bias_correction1]) |
| |
| bias_correction2_sqrt = [_dispatch_sqrt(bc) for bc in bias_correction2] |
| |
| if amsgrad: |
| # Maintains the maximum of all 2nd moment running avg. till now |
| torch._foreach_maximum_(device_max_exp_avg_sqs, device_exp_avg_sqs) |
| |
| # Use the max. for normalizing running avg. of gradient |
| max_exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs) |
| torch._foreach_div_(max_exp_avg_sq_sqrt, bias_correction2_sqrt) |
| denom = torch._foreach_add(max_exp_avg_sq_sqrt, eps) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs) |
| torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt) |
| denom = torch._foreach_add(exp_avg_sq_sqrt, eps) |
| |
| torch._foreach_addcdiv_(params_, device_exp_avgs, denom, step_size) |
| |
| |
| def _fused_adam( |
| params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| max_exp_avg_sqs: List[Tensor], |
| state_steps: List[Tensor], |
| grad_scale: Optional[Tensor], |
| found_inf: Optional[Tensor], |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, # Needed for consistency. |
| differentiable: bool, |
| ) -> None: |
| grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]) |
| grad_scale_dict = {grad_scale.device: grad_scale} if grad_scale is not None else None |
| found_inf_dict = {found_inf.device: found_inf} if found_inf is not None else None |
| grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps]) |
| for (device, dtype) in grouped_tensors: |
| ( |
| device_params, |
| device_grads, |
| device_exp_avgs, |
| device_exp_avg_sqs, |
| device_max_exp_avg_sqs, |
| device_state_steps, |
| ) = grouped_tensors[(device, dtype)] |
| if grad_scale is not None and found_inf is not None: |
| if device not in grad_scale_dict: |
| grad_scale_dict[device] = grad_scale.to(device, non_blocking=True) |
| if found_inf not in found_inf_dict: |
| found_inf_dict[device] = found_inf.to(device, non_blocking=True) |
| device_grad_scale = grad_scale_dict[device] |
| device_found_inf = found_inf_dict[device] |
| else: |
| device_grad_scale = None |
| device_found_inf = None |
| torch._foreach_add_(device_state_steps, 1) |
| torch._fused_adam_( |
| device_params, |
| device_grads, |
| device_exp_avgs, |
| device_exp_avg_sqs, |
| device_max_exp_avg_sqs, |
| device_state_steps, |
| amsgrad=amsgrad, |
| lr=lr, |
| beta1=beta1, |
| beta2=beta2, |
| weight_decay=weight_decay, |
| eps=eps, |
| maximize=maximize, |
| grad_scale=device_grad_scale, |
| found_inf=device_found_inf, |
| ) |
| if device_found_inf is not None: |
| torch._foreach_sub_(device_state_steps, [device_found_inf] * len(device_state_steps)) |
