162 lines
7.4 KiB
Python
162 lines
7.4 KiB
Python
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import torch
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from . import _functional as F
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from .optimizer import Optimizer, _maximize_doc
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__all__ = ['SparseAdam']
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class SparseAdam(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, maximize: bool = False):
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if not 0.0 < lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 < eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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defaults = dict(lr=lr, betas=betas, eps=eps, maximize=maximize)
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super().__init__(params, defaults)
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sparse_params = []
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complex_params = []
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for index, param_group in enumerate(self.param_groups):
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assert isinstance(param_group, dict), f"param_groups must be a list of dicts, but got {type(param_group)}"
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# given param group, convert given params to a list first before iterating
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for d_index, d_param in enumerate(param_group['params']):
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if d_param.is_sparse:
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sparse_params.append([index, d_index])
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if d_param.is_complex():
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complex_params.append([index, d_index])
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if sparse_params:
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raise ValueError(
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f"Sparse params at indices {sparse_params}: SparseAdam requires dense parameter tensors"
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)
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if complex_params:
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raise ValueError(
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f"Complex params at indices {complex_params}: SparseAdam does not support complex parameters"
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)
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@torch.no_grad()
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def step(self, closure=None):
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"""Perform a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_avg_sqs = []
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state_steps = []
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eps = group['eps']
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lr = group['lr']
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beta1, beta2 = group['betas']
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maximize = group.get('maximize', False)
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for p in group['params']:
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if p.grad is not None:
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params_with_grad.append(p)
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if not p.grad.is_sparse:
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raise RuntimeError('SparseAdam does not support dense gradients, please consider Adam instead')
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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exp_avgs.append(state['exp_avg'])
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exp_avg_sqs.append(state['exp_avg_sq'])
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# update the steps for each param group update
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state['step'] += 1
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# record the step after step update
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state_steps.append(state['step'])
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F.sparse_adam(params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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state_steps,
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beta1=beta1,
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beta2=beta2,
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lr=group['lr'],
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eps=group['eps'],
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maximize=maximize)
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return loss
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SparseAdam.__doc__ = fr"""SparseAdam implements a masked version of the Adam algorithm
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suitable for sparse gradients. Currently, due to implementation constraints (explained
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below), SparseAdam is only intended for a narrow subset of use cases, specifically
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parameters of a dense layout with gradients of a sparse layout. This occurs in a
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special case where the module backwards produces grads already in a sparse layout.
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One example NN module that behaves as such is ``nn.Embedding(sparse=True)``.
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SparseAdam approximates the Adam algorithm by masking out the parameter and moment
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updates corresponding to the zero values in the gradients. Whereas the Adam algorithm
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will update the first moment, the second moment, and the parameters based on all values
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of the gradients, SparseAdam only updates the moments and parameters corresponding
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to the non-zero values of the gradients.
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A simplified way of thinking about the `intended` implementation is as such:
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1. Create a mask of the non-zero values in the sparse gradients. For example,
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if your gradient looks like [0, 5, 0, 0, 9], the mask would be [0, 1, 0, 0, 1].
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2. Apply this mask over the running moments and do computation on only the
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non-zero values.
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3. Apply this mask over the parameters and only apply an update on non-zero values.
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In actuality, we use sparse layout Tensors to optimize this approximation, which means the
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more gradients that are masked by not being materialized, the more performant the optimization.
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Since we rely on using sparse layout tensors, we infer that any materialized value in the
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sparse layout is non-zero and we do NOT actually verify that all values are not zero!
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It is important to not conflate a semantically sparse tensor (a tensor where many
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of its values are zeros) with a sparse layout tensor (a tensor where ``.is_sparse``
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returns ``True``). The SparseAdam approximation is intended for `semantically` sparse
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tensors and the sparse layout is only a implementation detail. A clearer implementation
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would be to use MaskedTensors, but those are experimental.
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.. note::
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If you suspect your gradients are semantically sparse (but do not have sparse
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layout), this variant may not be the best for you. Ideally, you want to avoid
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materializing anything that is suspected to be sparse in the first place, since
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needing to convert all your grads from dense layout to sparse layout may outweigh
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the performance gain. Here, using Adam may be the best alternative, unless you
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can easily rig up your module to output sparse grads similar to
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``nn.Embedding(sparse=True)``. If you insist on converting your grads, you can do
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so by manually overriding your parameters' ``.grad`` fields with their sparse
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equivalents before calling ``.step()``.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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{_maximize_doc}
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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"""
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