Source code for deepspeed.ops.adam.fused_adam

# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team
Copyright NVIDIA/apex
This file is adapted from fused adam in NVIDIA/apex, commit 6bd01c4

import torch
from .multi_tensor_apply import MultiTensorApply

multi_tensor_applier = MultiTensorApply(2048 * 32)
from deepspeed.accelerator import get_accelerator
from deepspeed.ops.op_builder import FusedAdamBuilder

[docs]class FusedAdam(torch.optim.Optimizer): """Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via ``pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./``. This version of fused Adam implements 2 fusions. * Fusion of the Adam update's elementwise operations * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches. :class:`apex.optimizers.FusedAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``, or ``torch.optim.Adam`` with ``adam_w_mode=False``:: opt = apex.optimizers.FusedAdam(model.parameters(), lr = ....) ... opt.step() :class:`apex.optimizers.FusedAdam` may be used with or without Amp. If you wish to use :class:`FusedAdam` with Amp, you may choose any ``opt_level``:: opt = apex.optimizers.FusedAdam(model.parameters(), lr = ....) model, opt = amp.initialize(model, opt, opt_level="O0" or "O1 or "O2") ... opt.step() In general, ``opt_level="O1"`` is recommended. .. warning:: A previous version of :class:`FusedAdam` allowed a number of additional arguments to ``step``. These additional arguments are now deprecated and unnecessary. Adam was been proposed in `Adam: A Method for Stochastic Optimization`_. Arguments: 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 (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) NOT SUPPORTED in FusedAdam! adam_w_mode (boolean, optional): Apply L2 regularization or weight decay True for decoupled weight decay(also known as AdamW) (default: True) set_grad_none (bool, optional): whether set grad to None when zero_grad() method is called. (default: True) .. _Adam - A Method for Stochastic Optimization: .. _On the Convergence of Adam and Beyond: """ def __init__(self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-8, adam_w_mode=True, weight_decay=0., amsgrad=False, set_grad_none=True): if amsgrad: raise RuntimeError('FusedAdam does not support the AMSGrad variant.') defaults = dict(lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay) super(FusedAdam, self).__init__(params, defaults) self.adam_w_mode = 1 if adam_w_mode else 0 self.set_grad_none = set_grad_none fused_adam_cuda = FusedAdamBuilder().load() # Skip buffer self._dummy_overflow_buf = get_accelerator().IntTensor([0]) self.multi_tensor_adam = fused_adam_cuda.multi_tensor_adam def zero_grad(self): if self.set_grad_none: for group in self.param_groups: for p in group['params']: p.grad = None else: super(FusedAdam, self).zero_grad() def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None, grad_scaler=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. The remaining arguments are deprecated, and are only retained (for the moment) for error-checking purposes. """ if any(p is not None for p in [grads, output_params, scale, grad_norms]): raise RuntimeError( 'FusedAdam has been updated. Simply initialize it identically to torch.optim.Adam, and call step() with no arguments.' ) loss = None if closure is not None: loss = closure() for group in self.param_groups: if len(group['params']) == 0: continue bias_correction = 1 if group['bias_correction'] else 0 beta1, beta2 = group['betas'] # assume same step across group now to simplify things # per parameter step can be easily support by making it tensor, or pass list into kernel if 'step' not in group: group['step'] = 0 # create lists for multi-tensor apply g_16, p_16, m_16, v_16 = [], [], [], [] g_bf, p_bf, m_bf, v_bf = [], [], [], [] g_32, p_32, m_32, v_32 = [], [], [], [] for p in group['params']: if p.grad is None: continue if raise RuntimeError( 'FusedAdam does not support sparse gradients, please consider SparseAdam instead') state = self.state[p] # State initialization if len(state) == 0: # DeepSpeed ZeRO 3 processes each subgroup a time, so we need to keep tracking step count for each tensor separately. # While this is not an issue for ZeRO 1 & 2, since they apply a single optimization step to the whole param group at the same time. # In order to keep backward compatibility for the existing checkpoints, we use group['state'] to initialize state['step'] if it exists. state['step'] = group.get('step', 0) # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like( # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like( if p.dtype == torch.float16: g_16.append( p_16.append( m_16.append(state['exp_avg']) v_16.append(state['exp_avg_sq']) elif p.dtype == torch.bfloat16: g_bf.append(p.grad) p_bf.append(p) m_bf.append(state['exp_avg']) v_bf.append(state['exp_avg_sq']) elif p.dtype == torch.float32: g_32.append( p_32.append( m_32.append(state['exp_avg']) v_32.append(state['exp_avg_sq']) else: raise RuntimeError('FusedAdam only support fp16, bf16 and fp32.') if len(g_16) > 0: state['step'] += 1 multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_16, p_16, m_16, v_16], group['lr'], beta1, beta2, group['eps'], state['step'], self.adam_w_mode, bias_correction, group['weight_decay']) if len(g_bf) > 0: state['step'] += 1 multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_bf, p_bf, m_bf, v_bf], group['lr'], beta1, beta2, group['eps'], state['step'], self.adam_w_mode, bias_correction, group['weight_decay']) if len(g_32) > 0: state['step'] += 1 multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_32, p_32, m_32, v_32], group['lr'], beta1, beta2, group['eps'], state['step'], self.adam_w_mode, bias_correction, group['weight_decay']) return loss