From 5a184446925faaabff194a64cee33c409750b93d Mon Sep 17 00:00:00 2001 From: Justin Kerr Date: Mon, 23 Oct 2023 09:35:27 -0700 Subject: [PATCH] cleanup --- nerfstudio/engine/hacked_adam.py | 583 ------------------------------- 1 file changed, 583 deletions(-) delete mode 100644 nerfstudio/engine/hacked_adam.py diff --git a/nerfstudio/engine/hacked_adam.py b/nerfstudio/engine/hacked_adam.py deleted file mode 100644 index 6ddfa5e4a..000000000 --- a/nerfstudio/engine/hacked_adam.py +++ /dev/null @@ -1,583 +0,0 @@ -from typing import List, Optional - -import torch -from torch import Tensor -from torch.optim.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, _fused_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().__init__(params, defaults) - self.gauss_scaling = 1.0 - - 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 group['capturable'] or group['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. - """ - 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), - gauss_scaling=self.gauss_scaling - ) - - 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} - .. _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, fused=_fused_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, - gauss_scaling: float = 1.0 - ): - 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) - 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, - gauss_scaling=gauss_scaling - ) - - -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, - gauss_scaling: float = 1.0): - 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) - assert False - 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) - #apply the scaling here - exp_abs = device_exp_avgs[0].abs() - scaling = torch.ones_like(exp_abs) - biggers = (exp_abs>torch.tensor(gauss_scaling)).squeeze() - scaling[biggers] = (torch.tensor(gauss_scaling)/exp_abs)[biggers] - # scaling = torch.tensor(gauss_scaling) - device_exp_avgs = [x*scaling for i,x in enumerate(device_exp_avgs)] - 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: - 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)] - device_grad_scale, device_found_inf = None, None - if grad_scale is not None: - if device not in grad_scale_dict: - grad_scale_dict[device] = grad_scale.to(device, non_blocking=True) - device_grad_scale = grad_scale_dict[device] - if found_inf is not None: - if found_inf not in found_inf_dict: - found_inf_dict[device] = found_inf.to(device, non_blocking=True) - device_found_inf = found_inf_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))