| # mypy: allow-untyped-defs |
| from typing import List, Optional, Tuple, Union |
| |
| import torch |
| from torch import Tensor |
| from torch.utils._foreach_utils import _get_fused_kernels_supported_devices |
| from .optimizer import ( |
| _capturable_doc, |
| _default_to_fused_or_foreach, |
| _differentiable_doc, |
| _disable_dynamo_if_unsupported, |
| _dispatch_sqrt, |
| _foreach_doc, |
| _fused_doc, |
| _get_capturable_supported_devices, |
| _get_scalar_dtype, |
| _get_value, |
| _maximize_doc, |
| _stack_if_compiling, |
| _use_grad_for_differentiable, |
| _view_as_real, |
| DeviceDict, |
| Optimizer, |
| ParamsT, |
| ) |
| |
| __all__ = ["Adam", "adam"] |
| |
| |
| class Adam(Optimizer): |
| def __init__( |
| self, |
| params: ParamsT, |
| lr: Union[float, Tensor] = 1e-3, |
| betas: Tuple[float, float] = (0.9, 0.999), |
| eps: float = 1e-8, |
| weight_decay: float = 0, |
| amsgrad: bool = 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(f"Invalid learning rate: {lr}") |
| if isinstance(lr, Tensor) and foreach and not capturable: |
| raise ValueError( |
| "lr as a Tensor is not supported for capturable=False and foreach=True" |
| ) |
| if not 0.0 <= eps: |
| raise ValueError(f"Invalid epsilon value: {eps}") |
| if not 0.0 <= betas[0] < 1.0: |
| raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") |
| if not 0.0 <= betas[1] < 1.0: |
| raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") |
| if not 0.0 <= weight_decay: |
| raise ValueError(f"Invalid weight_decay value: {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().__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] |
| # Support 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. |
| fused_supported_devices = _get_fused_kernels_supported_devices() |
| if not all( |
| p.device.type in fused_supported_devices 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 floating point Tensors of " |
| f"supported devices: {fused_supported_devices}." |
| ) |
| 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) |
| fused = group.setdefault("fused", None) |
| for p in group["params"]: |
| p_state = self.state.get(p, []) |
| if len(p_state) != 0 and not torch.is_tensor(p_state["step"]): |
| step_val = float(p_state["step"]) |
| p_state["step"] = ( |
| torch.tensor( |
| step_val, |
| dtype=_get_scalar_dtype(is_fused=fused), |
| device=p.device, |
| ) |
| if group["capturable"] or group["fused"] |
| else torch.tensor(step_val, dtype=_get_scalar_dtype()) |
| ) |
| |
| def _init_group( |
| self, |
| group, |
| params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| max_exp_avg_sqs, |
| state_steps, |
| ): |
| has_complex = False |
| for p in group["params"]: |
| if p.grad is not None: |
| has_complex |= torch.is_complex(p) |
| 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: |
| # note(crcrpar): [special device hosting for step] |
| # Deliberately host `step` on CPU if both capturable and fused are off. |
| # This is because kernel launches are costly on CUDA and XLA. |
| state["step"] = ( |
| torch.zeros( |
| (), |
| dtype=_get_scalar_dtype(is_fused=group["fused"]), |
| device=p.device, |
| ) |
| if group["capturable"] or group["fused"] |
| else torch.tensor(0.0, dtype=_get_scalar_dtype()) |
| ) |
| # 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" |
| ) |
| |
| # Foreach without capturable does not support a tensor lr |
| if ( |
| group["foreach"] |
| and torch.is_tensor(group["lr"]) |
| and not group["capturable"] |
| ): |
| raise RuntimeError( |
| "lr as a Tensor is not supported for capturable=False and foreach=True" |
| ) |
| |
| state_steps.append(state["step"]) |
| return has_complex |
| |
| @_use_grad_for_differentiable |
| def step(self, closure=None): |
| """Perform a single optimization step. |
| |
| Args: |
| closure (Callable, optional): A closure that reevaluates the model |
| and returns the loss. |
| """ |
| 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: List[Tensor] = [] |
| grads: List[Tensor] = [] |
| exp_avgs: List[Tensor] = [] |
| exp_avg_sqs: List[Tensor] = [] |
| max_exp_avg_sqs: List[Tensor] = [] |
| state_steps: List[Tensor] = [] |
| beta1, beta2 = group["betas"] |
| |
| has_complex = 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"], |
| has_complex=has_complex, |
| 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`_. |
| """ |
| + rf""" |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR |
| is not yet supported for all our implementations. Please use a float |
| LR if you are not also specifying fused=True or capturable=True. |
| 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_doc} |
| {_maximize_doc} |
| {_capturable_doc} |
| {_differentiable_doc} |
| {_fused_doc} |
| .. Note:: |
| A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`. |
| .. _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 |
| |
| """ |
| ) |
| |
| |
| 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, |
| has_complex: bool, |
| beta1: float, |
| beta2: float, |
| lr: Union[float, Tensor], |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool, |
| ): |
| assert grad_scale is None and found_inf is None |
| |
| if torch.jit.is_scripting(): |
| # this assert is due to JIT being dumb and not realizing that the ops below |
| # have overloads to handle both float and Tensor lrs, so we just assert it's |
| # a float since most people using JIT are using floats |
| assert isinstance(lr, float) |
| |
| 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 compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] |
| if not torch._utils.is_compiling() and capturable: |
| capturable_supported_devices = _get_capturable_supported_devices() |
| assert ( |
| param.device.type == step_t.device.type |
| and param.device.type in capturable_supported_devices |
| ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." |
| |
| # 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) |
| if amsgrad: |
| max_exp_avg_sqs[i] = torch.view_as_real(max_exp_avg_sqs[i]) |
| param = torch.view_as_real(param) |
| |
| # Decay the first and second moment running average coefficient |
| exp_avg.lerp_(grad, 1 - beta1) |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad.conj(), value=1 - beta2) |
| |
| if capturable or differentiable: |
| step = step_t |
| |
| bias_correction1 = 1 - beta1**step |
| bias_correction2 = 1 - 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_sq = max_exp_avg_sqs[i].clone() |
| else: |
| max_exp_avg_sq = max_exp_avg_sqs[i] |
| |
| max_exp_avg_sqs[i].copy_(torch.maximum(max_exp_avg_sq, 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) |
| |
| # Lastly, switch back to complex view |
| if amsgrad and torch.is_complex(params[i]): |
| max_exp_avg_sqs[i] = torch.view_as_complex(max_exp_avg_sqs[i]) |
| |
| |
| 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, |
| has_complex: bool, |
| beta1: float, |
| beta2: float, |
| lr: Union[float, Tensor], |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, |
| differentiable: bool, |
| ): |
| if len(params) == 0: |
| return |
| |
| if isinstance(lr, Tensor) and not capturable: |
| raise RuntimeError( |
| "lr as a Tensor is not supported for capturable=False and foreach=True" |
| ) |
| |
| # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] |
| if not torch._utils.is_compiling() and capturable: |
| capturable_supported_devices = _get_capturable_supported_devices( |
| supports_xla=False |
| ) |
| assert all( |
| p.device.type == step.device.type |
| and p.device.type in capturable_supported_devices |
| for p, step in zip(params, state_steps) |
| ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." |
| |
| assert grad_scale is None and found_inf is None |
| |
| assert not differentiable, "_foreach ops don't support autograd" |
| |
| grouped_tensors = Optimizer._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(): |
| # Handle complex parameters |
| if has_complex: |
| if amsgrad: |
| _view_as_real( |
| device_params, |
| device_grads, |
| device_exp_avgs, |
| device_exp_avg_sqs, |
| device_max_exp_avg_sqs, |
| ) |
| else: |
| _view_as_real( |
| device_params, device_grads, device_exp_avgs, device_exp_avg_sqs |
| ) |
| |
| if maximize: |
| device_grads = torch._foreach_neg(device_grads) # type: ignore[assignment] |
| |
| # Update steps |
| # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over |
| # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just |
| # wrapped it once now. The alpha is required to assure we go to the right overload. |
| if device_state_steps[0].is_cpu: |
| torch._foreach_add_( |
| device_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0 |
| ) |
| else: |
| torch._foreach_add_(device_state_steps, 1) |
| |
| if weight_decay != 0: |
| # Re-use the intermediate memory (device_grads) already allocated for maximize |
| if maximize: |
| torch._foreach_add_(device_grads, device_params, alpha=weight_decay) |
| else: |
| device_grads = torch._foreach_add( # type: ignore[assignment] |
| device_grads, device_params, alpha=weight_decay |
| ) |
| |
| # Decay the first and second moment running average coefficient |
| torch._foreach_lerp_(device_exp_avgs, device_grads, 1 - beta1) |
| |
| torch._foreach_mul_(device_exp_avg_sqs, beta2) |
| torch._foreach_addcmul_( |
| device_exp_avg_sqs, device_grads, device_grads, 1 - beta2 |
| ) |
| |
| # Delete the local intermediate since it won't be used anymore to save on peak memory |
| del device_grads |
| |
| bias_correction1: Union[Tuple[Tensor, ...], List[Tensor]] |
| bias_correction2: Union[Tuple[Tensor, ...], List[Tensor]] |
| bias_correction2_sqrt: Union[Tuple[Tensor, ...], List[Tensor]] |
| if capturable: |
| bias_correction1 = torch._foreach_pow(beta1, device_state_steps) |
| bias_correction2 = torch._foreach_pow(beta2, 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) |
| # we do not negate bias_correction1 as it'll need to be negated later anyway |
| torch._foreach_neg_(bias_correction2) |
| |
| # foreach_div doesn't allow a scalar as the first arg |
| torch._foreach_div_(bias_correction1, lr) |
| torch._foreach_reciprocal_(bias_correction1) |
| |
| torch._foreach_sqrt_(bias_correction2) |
| |
| # Re-assign for clarity as we maintain minimal intermediates: we'll have |
| # step_size = - lr / (1 - beta1 ^ t) where t = num_steps |
| # bias_correction2_sqrt = sqrt(1 - beta2 ^ t) |
| step_size = bias_correction1 |
| bias_correction2_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] |
| |
| # Set intermediate to the max. for normalizing running avg. of gradient when amsgrad |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs) |
| |
| torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt) |
| torch._foreach_add_(exp_avg_sq_sqrt, eps) |
| torch._foreach_div_(exp_avg_sq_sqrt, step_size) |
| |
| # at this point, exp_avg_sq_sqrt = - (1 - beta^t) * [sqrt(exp_avg_sq / (1 - beta2^t)) + eps] / lr |
| torch._foreach_addcdiv_(device_params, device_exp_avgs, exp_avg_sq_sqrt) |
| 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] # type: ignore[arg-type] |
| |
| 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 |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_max_exp_avg_sqs) |
| else: |
| exp_avg_sq_sqrt = torch._foreach_sqrt(device_exp_avg_sqs) |
| |
| torch._foreach_div_(exp_avg_sq_sqrt, bias_correction2_sqrt) |
| torch._foreach_add_(exp_avg_sq_sqrt, eps) |
| torch._foreach_addcdiv_( |
| device_params, device_exp_avgs, exp_avg_sq_sqrt, step_size # type: ignore[arg-type] |
| ) |
| |
| |
| 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, |
| has_complex: bool, # Needed for consistency. |
| beta1: float, |
| beta2: float, |
| lr: Union[float, Tensor], |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| capturable: bool, # Needed for consistency. |
| differentiable: bool, |
| ) -> None: |
| if not params: |
| return |
| if differentiable: |
| raise RuntimeError("Adam with fused=True does not support differentiable=True") |
| |
| grad_scale_dict: DeviceDict = ( |
| {grad_scale.device: grad_scale} if grad_scale is not None else {} |
| ) |
| found_inf_dict: DeviceDict = ( |
| {found_inf.device: found_inf} if found_inf is not None else {} |
| ) |
| |
| # We only shuffle around the lr when it is a Tensor and on CUDA, otherwise, we prefer |
| # treating it as a scalar. |
| lr_dict: Optional[DeviceDict] = ( |
| {lr.device: lr} if isinstance(lr, Tensor) and str(lr.device) != "cpu" else None |
| ) |
| grouped_tensors = Optimizer._group_tensors_by_device_and_dtype( |
| [params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps] |
| ) |
| for (device, _), ( |
| ( |
| device_params, |
| device_grads, |
| device_exp_avgs, |
| device_exp_avg_sqs, |
| device_max_exp_avg_sqs, |
| device_state_steps, |
| ), |
| _, |
| ) in grouped_tensors.items(): |
| if device.type == "mps": # type: ignore[union-attr] |
| assert found_inf is None and grad_scale is None |
| assert not isinstance(lr, Tensor) |
| |
| device_grad_scale, device_found_inf = None, None |
| if grad_scale is not None: |
| device_grad_scale = grad_scale_dict.setdefault( |
| device, grad_scale.to(device, non_blocking=True) |
| ) |
| if found_inf is not None: |
| device_found_inf = found_inf_dict.setdefault( |
| device, found_inf.to(device, non_blocking=True) |
| ) |
| if lr_dict is not None and device not in lr_dict: |
| lr_dict[device] = lr.to(device=device, non_blocking=True) # type: ignore[union-attr] |
| lr = lr_dict[device] |
| 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) |
| ) |
| |
| |
| @_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adam) |
| 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, |
| has_complex: bool = False, |
| *, |
| amsgrad: bool, |
| beta1: float, |
| beta2: float, |
| lr: Union[float, Tensor], |
| weight_decay: float, |
| eps: float, |
| maximize: bool, |
| ): |
| r"""Functional API that performs Adam algorithm computation. |
| |
| See :class:`~torch.optim.Adam` for details. |
| """ |
| # Respect when the user inputs False/True for foreach or fused. We only want to change |
| # the default when neither have been user-specified. Note that we default to foreach |
| # and pass False to use_fused. This is not a mistake--we want to give the fused impl |
| # bake-in time before making it the default, even if it is typically faster. |
| if fused is None and foreach is None: |
| _, foreach = _default_to_fused_or_foreach( |
| params, differentiable, use_fused=False |
| ) |
| # Do not flip on foreach for the unsupported case where lr is a Tensor and capturable=False. |
| if foreach and isinstance(lr, Tensor) and not capturable: |
| foreach = False |
| if fused is None: |
| fused = False |
| if foreach is None: |
| foreach = False |
| |
| # this check is slow during compilation, so we skip it |
| # if it's strictly needed we can add this check back in dynamo |
| if not torch._utils.is_compiling() and 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 torch.jit.is_scripting(): |
| raise RuntimeError("torch.jit.script not supported with fused 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, |
| has_complex=has_complex, |
| 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, |
| ) |
