impl lamb

mmcls/core/optimizers/__init__.py

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+from .lamb import Lamb
+
+__all__ = ('Lamb', )

mmcls/core/optimizers/lamb.py

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+"""PyTorch Lamb optimizer w/ behaviour similar to NVIDIA FusedLamb.
+
+This optimizer code was adapted from the following (starting with latest)
+* https://github.com/HabanaAI/Model-References/blob/
+2b435114fe8e31f159b1d3063b8280ae37af7423/PyTorch/nlp/bert/pretraining/lamb.py
+* https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/
+LanguageModeling/Transformer-XL/pytorch/lamb.py
+* https://github.com/cybertronai/pytorch-lamb
+
+Use FusedLamb if you can (GPU). The reason for including this variant of Lamb
+is to have a version that is
+similar in behaviour to APEX FusedLamb if you aren't using NVIDIA GPUs or
+cannot install/use APEX.
+
+In addition to some cleanup, this Lamb impl has been modified to support
+PyTorch XLA and has been tested on TPU.
+
+Original copyrights for above sources are below.
+
+Modifications Copyright 2021 Ross Wightman
+"""
+# Copyright (c) 2021, Habana Labs Ltd.  All rights reserved.
+
+# Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# MIT License
+#
+# Copyright (c) 2019 cybertronai
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import math
+
+import torch
+from mmcv.runner import OPTIMIZERS
+from torch.optim import Optimizer
+
+
+@OPTIMIZERS.register_module()
+class Lamb(Optimizer):
+    """Implements a pure pytorch variant of FuseLAMB (NvLamb variant) optimizer
+    from apex.optimizers.FusedLAMB
+    reference: https://github.com/NVIDIA/DeepLearningExamples/blob/master/
+    PyTorch/LanguageModeling/Transformer-XL/pytorch/lamb.py
+
+    LAMB was proposed in `Large Batch Optimization for Deep Learning: Training
+    BERT in 76 minutes`_.
+
+    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 norm. (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)
+        grad_averaging (bool, optional): whether apply (1-beta2) to grad when
+            calculating running averages of gradient. (default: True)
+        max_grad_norm (float, optional): value used to clip global grad norm
+            (default: 1.0)
+        trust_clip (bool): enable LAMBC trust ratio clipping (default: False)
+        always_adapt (boolean, optional): Apply adaptive learning rate to 0.0
+            weight decay parameter (default: False)
+
+    .. _Large Batch Optimization for Deep Learning - Training BERT in 76
+        minutes:
+        https://arxiv.org/abs/1904.00962
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(self,
+                 params,
+                 lr=1e-3,
+                 bias_correction=True,
+                 betas=(0.9, 0.999),
+                 eps=1e-6,
+                 weight_decay=0.01,
+                 grad_averaging=True,
+                 max_grad_norm=1.0,
+                 trust_clip=False,
+                 always_adapt=False):
+        defaults = dict(
+            lr=lr,
+            bias_correction=bias_correction,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            grad_averaging=grad_averaging,
+            max_grad_norm=max_grad_norm,
+            trust_clip=trust_clip,
+            always_adapt=always_adapt)
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        device = self.param_groups[0]['params'][0].device
+        one_tensor = torch.tensor(
+            1.0, device=device
+        )  # because torch.where doesn't handle scalars correctly
+        global_grad_norm = torch.zeros(1, device=device)
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        'Lamb does not support sparse gradients, consider '
+                        'SparseAdam instead.')
+                global_grad_norm.add_(grad.pow(2).sum())
+
+        global_grad_norm = torch.sqrt(global_grad_norm)
+        # FIXME it'd be nice to remove explicit tensor conversion of scalars
+        #  when torch.where promotes
+        # scalar types properly https://github.com/pytorch/pytorch/issues/9190
+        max_grad_norm = torch.tensor(
+            self.defaults['max_grad_norm'], device=device)
+        clip_global_grad_norm = torch.where(global_grad_norm > max_grad_norm,
+                                            global_grad_norm / max_grad_norm,
+                                            one_tensor)
+
+        for group in self.param_groups:
+            bias_correction = 1 if group['bias_correction'] else 0
+            beta1, beta2 = group['betas']
+            grad_averaging = 1 if group['grad_averaging'] else 0
+            beta3 = 1 - beta1 if grad_averaging else 1.0
+
+            # 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' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            if bias_correction:
+                bias_correction1 = 1 - beta1**group['step']
+                bias_correction2 = 1 - beta2**group['step']
+            else:
+                bias_correction1, bias_correction2 = 1.0, 1.0
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.div_(clip_global_grad_norm)
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    # Exponential moving average of gradient valuesa
+                    state['exp_avg'] = torch.zeros_like(p)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(grad, alpha=beta3)  # m_t
+                exp_avg_sq.mul_(beta2).addcmul_(
+                    grad, grad, value=1 - beta2)  # v_t
+
+                denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
+                    group['eps'])
+                update = (exp_avg / bias_correction1).div_(denom)
+
+                weight_decay = group['weight_decay']
+                if weight_decay != 0:
+                    update.add_(p, alpha=weight_decay)
+
+                if weight_decay != 0 or group['always_adapt']:
+                    # Layer-wise LR adaptation. By default, skip adaptation on
+                    # parameters that are
+                    # excluded from weight decay, unless always_adapt == True,
+                    # then always enabled.
+                    w_norm = p.norm(2.0)
+                    g_norm = update.norm(2.0)
+                    # FIXME nested where required since logical and/or not
+                    #  working in PT XLA
+                    trust_ratio = torch.where(
+                        w_norm > 0,
+                        torch.where(g_norm > 0, w_norm / g_norm, one_tensor),
+                        one_tensor,
+                    )
+                    if group['trust_clip']:
+                        # LAMBC trust clipping, upper bound fixed at one
+                        trust_ratio = torch.minimum(trust_ratio, one_tensor)
+                    update.mul_(trust_ratio)
+
+                p.add_(update, alpha=-group['lr'])
+
+        return loss