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1c3fe84033
Support fused_sgd_kernel support for CPU. ## Bench result: 32 core/sockets ICX Test Scripts: https://gist.github.com/zhuhaozhe/79e842e0a6e25d6d7fa1e4598807272c https://gist.github.com/zhuhaozhe/b4c6998a509dcea1796dd05b3005c969 ``` Tensor Size: 262144, Num Tensor 4, Num Threads: 1 _single_tensor_adagrad time: 0.2500 seconds _fused_adagrad time: 0.0933 seconds Tensor Size: 4194304, Num Tensor 32, Num Threads: 32 _single_tensor_adagrad time: 2.8819 seconds _fused_adagrad time: 1.7591 seconds ``` ## Test Plan: ``` python test_optim.py -k test_fused_matches_forloop python test_optim.py -k test_fused_large_tensor python test_optim.py -k test_can_load_older_state_dict python test_optim.py -k test_grad_scaling_autocast_fused_optimizers python test_torch.py -k test_grad_scaling_autocast_fused python test_torch.py -k test_params_invalidated_with_grads_invalidated_between_unscale_and_step ``` Co-authored-by: Jane (Yuan) Xu <31798555+janeyx99@users.noreply.github.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/124905 Approved by: https://github.com/jgong5, https://github.com/janeyx99
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
from typing import Dict, List, Optional
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import torch
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import torch.optim._functional as F
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from torch import Tensor
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__all__: List[str] = []
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# Define a TorchScript compatible Functional Adagrad Optimizer
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# where we use these optimizer in a functional way.
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# Instead of using the `param.grad` when updating parameters,
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# we explicitly let the user pass gradients to the `step` function
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# this is so that we could separate the gradients and parameters
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# and allow multithreaded trainer to update the parameters
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# without data traces on accumulating to the same .grad.
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# NOTE: This should be only used by distributed optimizer internals
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# and not meant to expose to the user.
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@torch.jit.script
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class _FunctionalAdagrad:
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def __init__(
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self,
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params: List[Tensor],
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lr: float = 1e-2,
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lr_decay: float = 0.0,
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weight_decay: float = 0.0,
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initial_accumulator_value: float = 0.0,
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warmup_lr_multiplier: float = 1.0,
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warmup_num_iters: float = 0.0,
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eps: float = 1e-10,
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coalesce_grad: bool = True,
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foreach: bool = False,
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fused: bool = False,
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maximize: bool = False,
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_allow_empty_param_list: bool = False,
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):
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self.defaults = {
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"lr": lr,
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"lr_decay": lr_decay,
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"eps": eps,
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"weight_decay": weight_decay,
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"initial_accumulator_value": initial_accumulator_value,
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"warmup_lr_multiplier": warmup_lr_multiplier,
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"warmup_num_iters": warmup_num_iters,
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}
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self.coalesce_grad = coalesce_grad
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self.foreach = foreach
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self.fused = fused
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self.maximize = maximize
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self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
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if len(params) == 0 and not _allow_empty_param_list:
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raise ValueError("optimizer got an empty parameter list")
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# NOTE: we only have one param_group and don't allow user to add additional
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# param group as it's not a common use case.
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self.param_group = {"params": params}
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# TODO: no union or any types in TorchScript, make step a scalar tensor instead
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# This is also needed by if we want to share_memory on the step across processes
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for p in self.param_group["params"]:
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self.state[p] = {
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"sum": torch.full_like(p.data, initial_accumulator_value),
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"step": torch.tensor(0.0),
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}
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def step(self, gradients: List[Optional[Tensor]]):
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params = self.param_group["params"]
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params_with_grad = []
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grads = []
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state_sums = []
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state_steps: List[Tensor] = []
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if len(params) != len(gradients):
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raise ValueError(
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"the gradients passed in does not equal to the size of the parameters!"
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+ f"Params length: {len(params)}. "
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+ f"Gradients length: {len(gradients)}"
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)
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has_sparse_grad, has_complex = False, False
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for param, gradient in zip(self.param_group["params"], gradients):
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if gradient is not None:
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has_sparse_grad |= gradient.is_sparse
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has_complex |= torch.is_complex(param)
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params_with_grad.append(param)
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grads.append(gradient)
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state = self.state[param]
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state_sums.append(state["sum"])
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state_steps.append(state["step"])
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with torch.no_grad():
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F.adagrad(
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params,
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grads,
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state_sums,
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state_steps,
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lr=self.defaults["lr"],
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weight_decay=self.defaults["weight_decay"],
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lr_decay=self.defaults["lr_decay"],
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eps=self.defaults["eps"],
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has_sparse_grad=has_sparse_grad,
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foreach=self.foreach,
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maximize=self.maximize,
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has_complex=has_complex,
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fused=self.fused,
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grad_scale=None,
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found_inf=None,
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)
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