1124 lines
43 KiB
Python
1124 lines
43 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey)
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#
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# See ../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import contextlib
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import logging
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import random
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from collections import defaultdict
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from torch import Tensor
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from torch.optim import Optimizer
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class BatchedOptimizer(Optimizer):
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"""
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This class adds to class Optimizer the capability to optimize parameters in batches:
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it will stack the parameters and their grads for you so the optimizer can work
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on tensors with an extra leading dimension. This is intended for speed with GPUs,
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as it reduces the number of kernels launched in the optimizer.
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Args:
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params:
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"""
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def __init__(self, params, defaults):
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super(BatchedOptimizer, self).__init__(params, defaults)
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@contextlib.contextmanager
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def batched_params(self, param_group, group_params_names):
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"""
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This function returns (technically, yields) a list of
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of tuples (p, state), where
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p is a `fake` parameter that is stacked (over axis 0) from real parameters
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that share the same shape, and its gradient is also stacked;
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`state` is the state corresponding to this batch of parameters
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(it will be physically located in the "state" for one of the real
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parameters, the last one that has any particular shape and dtype).
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This function is decorated as a context manager so that it can
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write parameters back to their "real" locations.
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The idea is, instead of doing:
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<code>
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for p in group["params"]:
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state = self.state[p]
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...
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</code>
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you can do:
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<code>
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with self.batched_params(group["params"]) as batches:
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for p, state, p_names in batches:
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...
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</code>
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Args:
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group: a parameter group, which is a list of parameters; should be
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one of self.param_groups.
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group_params_names: name for each parameter in group,
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which is List[str].
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"""
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batches = defaultdict(
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list
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) # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
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batches_names = defaultdict(
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list
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) # `batches` maps from tuple (dtype_as_str,*shape) to list of str
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assert len(param_group) == len(group_params_names), f"len(param_group): {len(param_group)}, len(group_params_names): {len(group_params_names)}"
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for p, named_p in zip(param_group, group_params_names):
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key = (str(p.dtype), *p.shape)
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batches[key].append(p)
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batches_names[key].append(named_p)
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batches_names_keys = list(batches_names.keys())
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sorted_idx = sorted(
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range(len(batches_names)), key=lambda i: batches_names_keys[i]
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)
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batches_names = [
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batches_names[batches_names_keys[idx]] for idx in sorted_idx
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]
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batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
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stacked_params_dict = dict()
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# turn batches into a list, in deterministic order.
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# tuples will contain tuples of (stacked_param, state, stacked_params_names),
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# one for each batch in `batches`.
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tuples = []
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for batch, batch_names in zip(batches, batches_names):
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p = batch[0]
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# we arbitrarily store the state in the
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# state corresponding to the 1st parameter in the
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# group. class Optimizer will take care of saving/loading state.
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state = self.state[p]
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p_stacked = torch.stack(batch)
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grad = torch.stack(
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[
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torch.zeros_like(p) if p.grad is None else p.grad
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for p in batch
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]
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)
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p_stacked.grad = grad
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stacked_params_dict[key] = p_stacked
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tuples.append((p_stacked, state, batch_names))
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yield tuples # <-- calling code will do the actual optimization here!
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for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
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for i, p in enumerate(batch): # batch is list of Parameter
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p.copy_(stacked_params[i])
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class ScaledAdam(BatchedOptimizer):
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"""
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Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
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proportional to the norm of that parameter; and also learn the scale of the parameter,
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in log space, subject to upper and lower limits (as if we had factored each parameter as
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param = underlying_param * log_scale.exp())
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Args:
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params: The parameters or param_groups to optimize (like other Optimizer subclasses)
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lr: The learning rate. We will typically use a learning rate schedule that starts
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at 0.03 and decreases over time, i.e. much higher than other common
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optimizers.
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clipping_scale: (e.g. 2.0)
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A scale for gradient-clipping: if specified, the normalized gradients
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over the whole model will be clipped to have 2-norm equal to
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`clipping_scale` times the median 2-norm over the most recent period
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of `clipping_update_period` minibatches. By "normalized gradients",
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we mean after multiplying by the rms parameter value for this tensor
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[for non-scalars]; this is appropriate because our update is scaled
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by this quantity.
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betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
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Must satisfy 0 < beta <= beta2 < 1.
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scalar_lr_scale: A scaling factor on the learning rate, that we use to update the
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scale of each parameter tensor and scalar parameters of the mode..
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If each parameter were decomposed
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as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
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would be a the scaling factor on the learning rate of p_scale.
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eps: A general-purpose epsilon to prevent division by zero
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param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
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learning the scale on the parameters (we'll constrain the rms of each non-scalar
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parameter tensor to be >= this value)
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param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
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learning the scale on the parameters (we'll constrain the rms of each non-scalar
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parameter tensor to be <= this value)
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scalar_max: Maximum absolute value for scalar parameters (applicable if your
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model has any parameters with numel() == 1).
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size_update_period: The periodicity, in steps, with which we update the size (scale)
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of the parameter tensor. This is provided to save a little time
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in the update.
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clipping_update_period: if clipping_scale is specified, this is the period
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"""
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def __init__(
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self,
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params,
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lr=3e-02,
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clipping_scale=None,
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betas=(0.9, 0.98),
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scalar_lr_scale=0.1,
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eps=1.0e-08,
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param_min_rms=1.0e-05,
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param_max_rms=3.0,
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scalar_max=10.0,
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size_update_period=4,
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clipping_update_period=100,
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parameters_names=None,
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show_dominant_parameters=True,
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):
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assert parameters_names is not None, (
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"Please prepare parameters_names,"
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"which is a List[List[str]]. Each List[str] is for a group"
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"and each str is for a parameter"
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)
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defaults = dict(
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lr=lr,
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clipping_scale=clipping_scale,
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betas=betas,
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scalar_lr_scale=scalar_lr_scale,
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eps=eps,
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param_min_rms=param_min_rms,
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param_max_rms=param_max_rms,
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scalar_max=scalar_max,
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size_update_period=size_update_period,
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clipping_update_period=clipping_update_period,
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)
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super(ScaledAdam, self).__init__(params, defaults)
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assert len(self.param_groups) == len(parameters_names)
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self.parameters_names = parameters_names
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self.show_dominant_parameters = show_dominant_parameters
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def __setstate__(self, state):
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super(ScaledAdam, self).__setstate__(state)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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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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batch = True
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for group, group_params_names in zip(
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self.param_groups, self.parameters_names
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):
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with self.batched_params(
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group["params"], group_params_names
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) as batches:
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# batches is list of pairs (stacked_param, state). stacked_param is like
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# a regular parameter, and will have a .grad, but the 1st dim corresponds to
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# a stacking dim, it is not a real dim.
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if (
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len(batches[0][1]) == 0
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): # if len(first state) == 0: not yet initialized
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clipping_scale = 1
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else:
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clipping_scale = self._get_clipping_scale(group, batches)
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for p, state, _ in batches:
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# Perform optimization step.
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# grad is not going to be None, we handled that when creating the batches.
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError(
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"ScaledAdam optimizer does not support sparse gradients"
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)
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# State initialization
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if len(state) == 0:
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self._init_state(group, p, state)
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self._step_one_batch(group, p, state, clipping_scale)
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return loss
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def _init_state(self, group: dict, p: Tensor, state: dict):
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"""
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Initializes state dict for parameter 'p'. Assumes that dim 0 of tensor p
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is actually the batch dimension, corresponding to batched-together
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parameters of a given shape.
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Args:
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group: Dict to look up configuration values.
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p: The parameter that we are initializing the state for
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state: Dict from string to whatever state we are initializing
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"""
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size_update_period = group["size_update_period"]
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state["step"] = 0
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kwargs = {"device": p.device, "dtype": p.dtype}
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# 'delta' implements conventional momentum. There are
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# several different kinds of update going on, so rather than
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# compute "exp_avg" like in Adam, we store and decay a
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# parameter-change "delta", which combines all forms of
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# update. this is equivalent to how it's done in Adam,
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# except for the first few steps.
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state["delta"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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batch_size = p.shape[0]
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numel = p.numel() // batch_size
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numel = p.numel()
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if numel > 1:
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# "param_rms" just periodically records the scalar root-mean-square value of
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# the parameter tensor.
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# it has a shape like (batch_size, 1, 1, 1, 1)
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param_rms = (
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(p ** 2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
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)
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state["param_rms"] = param_rms
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state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
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state["scale_grads"] = torch.zeros(
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size_update_period, *param_rms.shape, **kwargs
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)
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# exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
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state["exp_avg_sq"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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def _get_clipping_scale(
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self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
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) -> float:
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"""
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Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
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by this amount before applying the rest of the update.
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Args:
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group: the parameter group, an item in self.param_groups
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tuples: a list of tuples of (param, state, param_names)
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where param is a batched set of parameters,
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with a .grad (1st dim is batch dim)
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and state is the state-dict where optimization parameters are kept.
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param_names is a List[str] while each str is name for a parameter
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in batched set of parameters "param".
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"""
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assert len(tuples) >= 1
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clipping_scale = group["clipping_scale"]
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(first_p, first_state, _) = tuples[0]
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step = first_state["step"]
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if clipping_scale is None or step == 0:
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# no clipping. return early on step == 0 because the other
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# parameters' state won't have been initialized yet.
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return 1.0
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clipping_update_period = group["clipping_update_period"]
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tot_sumsq = torch.tensor(0.0, device=first_p.device)
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for (p, state, param_names) in tuples:
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError(
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"ScaledAdam optimizer does not support sparse gradients"
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)
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if p.numel() == p.shape[0]: # a batch of scalars
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tot_sumsq += (
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grad ** 2
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).sum() # sum() to change shape [1] to []
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else:
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tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
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tot_norm = tot_sumsq.sqrt()
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if "model_norms" not in first_state:
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first_state["model_norms"] = torch.zeros(
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clipping_update_period, device=p.device
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)
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first_state["model_norms"][step % clipping_update_period] = tot_norm
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if step % clipping_update_period == 0:
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# Print some stats.
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# We don't reach here if step == 0 because we would have returned
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# above.
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sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
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quartiles = []
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for n in range(0, 5):
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index = min(
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clipping_update_period - 1,
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(clipping_update_period // 4) * n,
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)
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quartiles.append(sorted_norms[index].item())
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median = quartiles[2]
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threshold = clipping_scale * median
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first_state["model_norm_threshold"] = threshold
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percent_clipped = (
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first_state["num_clipped"] * 100.0 / clipping_update_period
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if "num_clipped" in first_state
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else 0.0
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)
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first_state["num_clipped"] = 0
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quartiles = " ".join(["%.3e" % x for x in quartiles])
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logging.info(
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f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
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f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
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)
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if step < clipping_update_period:
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return 1.0 # We have not yet estimated a norm to clip to.
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else:
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try:
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model_norm_threshold = first_state["model_norm_threshold"]
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except KeyError:
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logging.info(
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"Warning: model_norm_threshold not in state: possibly "
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"you changed config when restarting, adding clipping_scale option?"
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)
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return 1.0
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ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
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if ans < 1.0:
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first_state["num_clipped"] += 1
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if ans < 0.1:
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logging.warn(
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f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
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)
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if self.show_dominant_parameters:
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assert p.shape[0] == len(param_names)
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self._show_gradient_dominating_parameter(tuples, tot_sumsq)
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return ans
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def _show_gradient_dominating_parameter(
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self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor
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):
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"""
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Show information of parameter wihch dominanting tot_sumsq.
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Args:
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tuples: a list of tuples of (param, state, param_names)
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where param is a batched set of parameters,
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with a .grad (1st dim is batch dim)
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and state is the state-dict where optimization parameters are kept.
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param_names is a List[str] while each str is name for a parameter
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in batched set of parameters "param".
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tot_sumsq: sumsq of all parameters. Though it's could be calculated
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from tuples, we still pass it to save some time.
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"""
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all_sumsq_orig = {}
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for (p, state, batch_param_names) in tuples:
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# p is a stacked batch parameters.
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batch_grad = p.grad
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if p.numel() == p.shape[0]: # a batch of scalars
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batch_sumsq_orig = batch_grad ** 2
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# Dummpy values used by following `zip` statement.
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batch_rms_orig = torch.ones(p.shape[0])
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else:
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batch_rms_orig = state["param_rms"]
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batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum(
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dim=list(range(1, batch_grad.ndim))
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)
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for name, sumsq_orig, rms, grad in zip(
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batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
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):
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proportion_orig = sumsq_orig / tot_sumsq
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all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
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assert torch.isclose(
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sum([value[0] for value in all_sumsq_orig.values()]).cpu(),
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torch.tensor(1.0),
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)
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sorted_by_proportion = {
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k: v
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for k, v in sorted(
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all_sumsq_orig.items(),
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key=lambda item: item[1][0],
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reverse=True,
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)
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}
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dominant_param_name = next(iter(sorted_by_proportion))
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(
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dominant_proportion,
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dominant_sumsq,
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dominant_rms,
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dominant_grad,
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) = sorted_by_proportion[dominant_param_name]
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logging.info(
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f"Parameter Dominanting tot_sumsq {dominant_param_name}"
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f" with proportion {dominant_proportion:.2f},"
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f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
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f"={dominant_sumsq:.3e},"
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f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
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f" orig_rms_sq={(dominant_rms**2).item():.3e}"
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)
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def _step_one_batch(
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self, group: dict, p: Tensor, state: dict, clipping_scale: float
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):
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"""
|
|
Do the step for one parameter, which is actually going to be a batch of
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`real` parameters, with dim 0 as the batch dim.
|
|
Args:
|
|
group: dict to look up configuration values
|
|
p: parameter to update (actually multiple parameters stacked together
|
|
as a batch)
|
|
state: state-dict for p, to look up the optimizer state
|
|
"""
|
|
lr = group["lr"]
|
|
size_update_period = group["size_update_period"]
|
|
beta1 = group["betas"][0]
|
|
|
|
grad = p.grad
|
|
if clipping_scale != 1.0:
|
|
grad = grad * clipping_scale
|
|
step = state["step"]
|
|
delta = state["delta"]
|
|
|
|
delta.mul_(beta1)
|
|
batch_size = p.shape[0]
|
|
numel = p.numel() // batch_size
|
|
if numel > 1:
|
|
# Update the size/scale of p, and set param_rms
|
|
scale_grads = state["scale_grads"]
|
|
scale_grads[step % size_update_period] = (p * grad).sum(
|
|
dim=list(range(1, p.ndim)), keepdim=True
|
|
)
|
|
if step % size_update_period == size_update_period - 1:
|
|
param_rms = state["param_rms"] # shape: (batch_size, 1, 1, ..)
|
|
param_rms.copy_(
|
|
(p ** 2)
|
|
.mean(dim=list(range(1, p.ndim)), keepdim=True)
|
|
.sqrt()
|
|
)
|
|
if step > 0:
|
|
# self._size_update() learns the overall scale on the
|
|
# parameter, by shrinking or expanding it.
|
|
self._size_update(group, scale_grads, p, state)
|
|
|
|
if numel == 1:
|
|
# For parameters with 1 element we just use regular Adam.
|
|
# Updates delta.
|
|
self._step_scalar(group, p, state)
|
|
else:
|
|
self._step(group, p, state)
|
|
|
|
state["step"] = step + 1
|
|
|
|
def _size_update(
|
|
self, group: dict, scale_grads: Tensor, p: Tensor, state: dict
|
|
) -> None:
|
|
"""
|
|
Called only where p.numel() > 1, this updates the scale of the parameter.
|
|
If we imagine: p = underlying_param * scale.exp(), and we are doing
|
|
gradient descent on underlying param and on scale, this function does the update
|
|
on `scale`.
|
|
|
|
Args:
|
|
group: dict to look up configuration values
|
|
scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing
|
|
grads w.r.t. the scales.
|
|
p: The parameter to update
|
|
state: The state-dict of p
|
|
"""
|
|
|
|
param_rms = state["param_rms"]
|
|
beta1, beta2 = group["betas"]
|
|
size_lr = group["lr"] * group["scalar_lr_scale"]
|
|
param_min_rms = group["param_min_rms"]
|
|
param_max_rms = group["param_max_rms"]
|
|
eps = group["eps"]
|
|
step = state["step"]
|
|
batch_size = p.shape[0]
|
|
|
|
size_update_period = scale_grads.shape[0]
|
|
# correct beta2 for the size update period: we will have
|
|
# faster decay at this level.
|
|
beta2_corr = beta2 ** size_update_period
|
|
|
|
scale_exp_avg_sq = state[
|
|
"scale_exp_avg_sq"
|
|
] # shape: (batch_size, 1, 1, ..)
|
|
scale_exp_avg_sq.mul_(beta2_corr).add_(
|
|
(scale_grads ** 2).mean(
|
|
dim=0
|
|
), # mean over dim `size_update_period`
|
|
alpha=1 - beta2_corr,
|
|
) # shape is (batch_size, 1, 1, ...)
|
|
|
|
# The 1st time we reach here is when size_step == 1.
|
|
size_step = (step + 1) // size_update_period
|
|
bias_correction2 = 1 - beta2_corr ** size_step
|
|
# we don't bother with bias_correction1; this will help prevent divergence
|
|
# at the start of training.
|
|
|
|
denom = scale_exp_avg_sq.sqrt() + eps
|
|
|
|
scale_step = (
|
|
-size_lr
|
|
* (bias_correction2 ** 0.5)
|
|
* scale_grads.sum(dim=0)
|
|
/ denom
|
|
)
|
|
|
|
is_too_small = param_rms < param_min_rms
|
|
is_too_large = param_rms > param_max_rms
|
|
|
|
# when the param gets too small, just don't shrink it any further.
|
|
scale_step.masked_fill_(is_too_small, 0.0)
|
|
# when it gets too large, stop it from getting any larger.
|
|
scale_step.masked_fill_(is_too_large, -size_lr * size_update_period)
|
|
delta = state["delta"]
|
|
# the factor of (1-beta1) relates to momentum.
|
|
delta.add_(p * scale_step, alpha=(1 - beta1))
|
|
|
|
def _step(self, group: dict, p: Tensor, state: dict):
|
|
"""
|
|
This function does the core update of self.step(), in the case where the members of
|
|
the batch have more than 1 element.
|
|
|
|
Args:
|
|
group: A dict which will be used to look up configuration values
|
|
p: The parameter to be updated
|
|
grad: The grad of p
|
|
state: The state-dict corresponding to parameter p
|
|
|
|
This function modifies p.
|
|
"""
|
|
grad = p.grad
|
|
lr = group["lr"]
|
|
beta1, beta2 = group["betas"]
|
|
eps = group["eps"]
|
|
param_min_rms = group["param_min_rms"]
|
|
step = state["step"]
|
|
|
|
exp_avg_sq = state["exp_avg_sq"]
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
|
|
|
|
this_step = state["step"] - (
|
|
state["zero_step"] if "zero_step" in state else 0
|
|
)
|
|
bias_correction2 = 1 - beta2 ** (this_step + 1)
|
|
if bias_correction2 < 0.99:
|
|
# note: not in-place.
|
|
exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
|
|
|
|
denom = exp_avg_sq.sqrt()
|
|
denom += eps
|
|
grad = grad / denom
|
|
|
|
alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms)
|
|
|
|
delta = state["delta"]
|
|
delta.add_(grad * alpha)
|
|
p.add_(delta)
|
|
|
|
def _step_scalar(self, group: dict, p: Tensor, state: dict):
|
|
"""
|
|
A simplified form of the core update for scalar tensors, where we cannot get a good
|
|
estimate of the parameter rms.
|
|
"""
|
|
beta1, beta2 = group["betas"]
|
|
scalar_max = group["scalar_max"]
|
|
eps = group["eps"]
|
|
lr = group["lr"] * group["scalar_lr_scale"]
|
|
grad = p.grad
|
|
|
|
exp_avg_sq = state["exp_avg_sq"] # shape: (batch_size,)
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
|
|
|
|
# bias_correction2 is like in Adam. Don't bother with bias_correction1;
|
|
# slower update at the start will help stability anyway.
|
|
bias_correction2 = 1 - beta2 ** (state["step"] + 1)
|
|
denom = (exp_avg_sq / bias_correction2).sqrt() + eps
|
|
|
|
delta = state["delta"]
|
|
delta.add_(grad / denom, alpha=-lr * (1 - beta1))
|
|
p.clamp_(min=-scalar_max, max=scalar_max)
|
|
p.add_(delta)
|
|
|
|
|
|
class LRScheduler(object):
|
|
"""
|
|
Base-class for learning rate schedulers where the learning-rate depends on both the
|
|
batch and the epoch.
|
|
"""
|
|
|
|
def __init__(self, optimizer: Optimizer, verbose: bool = False):
|
|
# Attach optimizer
|
|
if not isinstance(optimizer, Optimizer):
|
|
raise TypeError(
|
|
"{} is not an Optimizer".format(type(optimizer).__name__)
|
|
)
|
|
self.optimizer = optimizer
|
|
self.verbose = verbose
|
|
|
|
for group in optimizer.param_groups:
|
|
group.setdefault("base_lr", group["lr"])
|
|
|
|
self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
|
|
|
|
self.epoch = 0
|
|
self.batch = 0
|
|
|
|
def state_dict(self):
|
|
"""Returns the state of the scheduler as a :class:`dict`.
|
|
|
|
It contains an entry for every variable in self.__dict__ which
|
|
is not the optimizer.
|
|
"""
|
|
return {
|
|
"base_lrs": self.base_lrs,
|
|
"epoch": self.epoch,
|
|
"batch": self.batch,
|
|
}
|
|
|
|
def load_state_dict(self, state_dict):
|
|
"""Loads the schedulers state.
|
|
|
|
Args:
|
|
state_dict (dict): scheduler state. Should be an object returned
|
|
from a call to :meth:`state_dict`.
|
|
"""
|
|
self.__dict__.update(state_dict)
|
|
|
|
def get_last_lr(self) -> List[float]:
|
|
"""Return last computed learning rate by current scheduler. Will be a list of float."""
|
|
return self._last_lr
|
|
|
|
def get_lr(self):
|
|
# Compute list of learning rates from self.epoch and self.batch and
|
|
# self.base_lrs; this must be overloaded by the user.
|
|
# e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
|
|
raise NotImplementedError
|
|
|
|
def step_batch(self, batch: Optional[int] = None) -> None:
|
|
# Step the batch index, or just set it. If `batch` is specified, it
|
|
# must be the batch index from the start of training, i.e. summed over
|
|
# all epochs.
|
|
# You can call this in any order; if you don't provide 'batch', it should
|
|
# of course be called once per batch.
|
|
if batch is not None:
|
|
self.batch = batch
|
|
else:
|
|
self.batch = self.batch + 1
|
|
self._set_lrs()
|
|
|
|
def step_epoch(self, epoch: Optional[int] = None):
|
|
# Step the epoch index, or just set it. If you provide the 'epoch' arg,
|
|
# you should call this at the start of the epoch; if you don't provide the 'epoch'
|
|
# arg, you should call it at the end of the epoch.
|
|
if epoch is not None:
|
|
self.epoch = epoch
|
|
else:
|
|
self.epoch = self.epoch + 1
|
|
self._set_lrs()
|
|
|
|
def _set_lrs(self):
|
|
values = self.get_lr()
|
|
assert len(values) == len(self.optimizer.param_groups)
|
|
|
|
for i, data in enumerate(zip(self.optimizer.param_groups, values)):
|
|
param_group, lr = data
|
|
param_group["lr"] = lr
|
|
self.print_lr(self.verbose, i, lr)
|
|
self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
|
|
|
|
def print_lr(self, is_verbose, group, lr):
|
|
"""Display the current learning rate."""
|
|
if is_verbose:
|
|
logging.info(
|
|
f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
|
|
f" of group {group} to {lr:.4e}."
|
|
)
|
|
|
|
|
|
class Eden(LRScheduler):
|
|
"""
|
|
Eden scheduler.
|
|
The basic formula (before warmup) is:
|
|
lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
|
|
(((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
|
|
where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
|
|
and then stays constant at 1.
|
|
|
|
|
|
E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
|
|
|
|
Args:
|
|
optimizer: the optimizer to change the learning rates on
|
|
lr_batches: the number of batches after which we start significantly
|
|
decreasing the learning rate, suggest 5000.
|
|
lr_epochs: the number of epochs after which we start significantly
|
|
decreasing the learning rate, suggest 6 if you plan to do e.g.
|
|
20 to 40 epochs, but may need smaller number if dataset is huge
|
|
and you will do few epochs.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
optimizer: Optimizer,
|
|
lr_batches: Union[int, float],
|
|
lr_epochs: Union[int, float],
|
|
warmup_batches: Union[int, float] = 500.0,
|
|
verbose: bool = False,
|
|
):
|
|
super(Eden, self).__init__(optimizer, verbose)
|
|
self.lr_batches = lr_batches
|
|
self.lr_epochs = lr_epochs
|
|
self.warmup_batches = warmup_batches
|
|
|
|
def get_lr(self):
|
|
factor = (
|
|
(self.batch ** 2 + self.lr_batches ** 2) / self.lr_batches ** 2
|
|
) ** -0.25 * (
|
|
((self.epoch ** 2 + self.lr_epochs ** 2) / self.lr_epochs ** 2)
|
|
** -0.25
|
|
)
|
|
warmup_factor = (
|
|
1.0
|
|
if self.batch >= self.warmup_batches
|
|
else 0.5 + 0.5 * (self.batch / self.warmup_batches)
|
|
)
|
|
|
|
return [x * factor * warmup_factor for x in self.base_lrs]
|
|
|
|
|
|
def _test_eden():
|
|
m = torch.nn.Linear(100, 100)
|
|
optim = ScaledAdam(m.parameters(), lr=0.03)
|
|
|
|
scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
|
|
|
|
for epoch in range(10):
|
|
scheduler.step_epoch(epoch) # sets epoch to `epoch`
|
|
|
|
for step in range(20):
|
|
x = torch.randn(200, 100).detach()
|
|
x.requires_grad = True
|
|
y = m(x)
|
|
dy = torch.randn(200, 100).detach()
|
|
f = (y * dy).sum()
|
|
f.backward()
|
|
|
|
optim.step()
|
|
scheduler.step_batch()
|
|
optim.zero_grad()
|
|
|
|
logging.info(f"last lr = {scheduler.get_last_lr()}")
|
|
logging.info(f"state dict = {scheduler.state_dict()}")
|
|
|
|
|
|
# This is included mostly as a baseline for ScaledAdam.
|
|
class Eve(Optimizer):
|
|
"""
|
|
Implements Eve algorithm. This is a modified version of AdamW with a special
|
|
way of setting the weight-decay / shrinkage-factor, which is designed to make the
|
|
rms of the parameters approach a particular target_rms (default: 0.1). This is
|
|
for use with networks with 'scaled' versions of modules (see scaling.py), which
|
|
will be close to invariant to the absolute scale on the parameter matrix.
|
|
|
|
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
|
|
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
|
|
Eve is unpublished so far.
|
|
|
|
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 coefficient (default: 3e-4;
|
|
this value means that the weight would decay significantly after
|
|
about 3k minibatches. Is not multiplied by learning rate, but
|
|
is conditional on RMS-value of parameter being > target_rms.
|
|
target_rms (float, optional): target root-mean-square value of
|
|
parameters, if they fall below this we will stop applying weight decay.
|
|
|
|
|
|
.. _Adam: A Method for Stochastic Optimization:
|
|
https://arxiv.org/abs/1412.6980
|
|
.. _Decoupled Weight Decay Regularization:
|
|
https://arxiv.org/abs/1711.05101
|
|
.. _On the Convergence of Adam and Beyond:
|
|
https://openreview.net/forum?id=ryQu7f-RZ
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
params,
|
|
lr=1e-3,
|
|
betas=(0.9, 0.98),
|
|
eps=1e-8,
|
|
weight_decay=1e-3,
|
|
target_rms=0.1,
|
|
):
|
|
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 <= weight_decay <= 0.1:
|
|
raise ValueError(
|
|
"Invalid weight_decay value: {}".format(weight_decay)
|
|
)
|
|
if not 0 < target_rms <= 10.0:
|
|
raise ValueError("Invalid target_rms value: {}".format(target_rms))
|
|
defaults = dict(
|
|
lr=lr,
|
|
betas=betas,
|
|
eps=eps,
|
|
weight_decay=weight_decay,
|
|
target_rms=target_rms,
|
|
)
|
|
super(Eve, self).__init__(params, defaults)
|
|
|
|
def __setstate__(self, state):
|
|
super(Eve, self).__setstate__(state)
|
|
|
|
@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()
|
|
|
|
for group in self.param_groups:
|
|
for p in group["params"]:
|
|
if p.grad is None:
|
|
continue
|
|
|
|
# Perform optimization step
|
|
grad = p.grad
|
|
if grad.is_sparse:
|
|
raise RuntimeError(
|
|
"AdamW does not support sparse gradients"
|
|
)
|
|
|
|
state = self.state[p]
|
|
|
|
# State initialization
|
|
if len(state) == 0:
|
|
state["step"] = 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
|
|
)
|
|
|
|
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
|
|
|
|
beta1, beta2 = group["betas"]
|
|
|
|
state["step"] += 1
|
|
bias_correction1 = 1 - beta1 ** state["step"]
|
|
bias_correction2 = 1 - beta2 ** state["step"]
|
|
|
|
# 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, value=1 - beta2)
|
|
denom = (exp_avg_sq.sqrt() * (bias_correction2 ** -0.5)).add_(
|
|
group["eps"]
|
|
)
|
|
|
|
step_size = group["lr"] / bias_correction1
|
|
target_rms = group["target_rms"]
|
|
weight_decay = group["weight_decay"]
|
|
|
|
if p.numel() > 1:
|
|
# avoid applying this weight-decay on "scaling factors"
|
|
# (which are scalar).
|
|
is_above_target_rms = p.norm() > (
|
|
target_rms * (p.numel() ** 0.5)
|
|
)
|
|
p.mul_(1 - (weight_decay * is_above_target_rms))
|
|
|
|
p.addcdiv_(exp_avg, denom, value=-step_size)
|
|
|
|
# if random.random() < 0.0005:
|
|
# step = (exp_avg / denom) * step_size
|
|
# logging.info(
|
|
# f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}"
|
|
# )
|
|
|
|
return loss
|
|
|
|
def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
|
|
"""
|
|
Behaves like a constructor of a modified version of nn.Linear
|
|
that gives an easy way to set the default initial parameter scale.
|
|
|
|
Args:
|
|
Accepts the standard args and kwargs that nn.Linear accepts
|
|
e.g. in_features, out_features, bias=False.
|
|
|
|
initial_scale: you can override this if you want to increase
|
|
or decrease the initial magnitude of the module's output
|
|
(affects the initialization of weight_scale and bias_scale).
|
|
Another option, if you want to do something like this, is
|
|
to re-initialize the parameters.
|
|
"""
|
|
ans = nn.Linear(*args, **kwargs)
|
|
with torch.no_grad():
|
|
ans.weight[:] *= initial_scale
|
|
if ans.bias is not None:
|
|
torch.nn.init.uniform_(
|
|
ans.bias, -0.1 * initial_scale, 0.1 * initial_scale
|
|
)
|
|
return ans
|
|
def _test_scaled_adam(hidden_dim: int):
|
|
import timeit
|
|
|
|
E = 100
|
|
B = 4
|
|
T = 2
|
|
logging.info("in test_eve_cain")
|
|
# device = torch.device('cuda')
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32
|
|
|
|
# these input_magnitudes and output_magnitudes are to test that
|
|
# Abel is working as we expect and is able to adjust scales of
|
|
# different dims differently.
|
|
input_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
|
|
output_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
|
|
|
|
for iter in [1, 0]:
|
|
Linear = torch.nn.Linear if iter == 0 else ScaledLinear
|
|
|
|
m = torch.nn.Sequential(
|
|
Linear(E, hidden_dim),
|
|
torch.nn.PReLU(),
|
|
Linear(hidden_dim, hidden_dim),
|
|
torch.nn.PReLU(),
|
|
Linear(hidden_dim, E),
|
|
).to(device)
|
|
|
|
train_pairs = [
|
|
(
|
|
100.0
|
|
* torch.randn(B, T, E, device=device, dtype=dtype)
|
|
* input_magnitudes,
|
|
torch.randn(B, T, E, device=device, dtype=dtype)
|
|
* output_magnitudes,
|
|
)
|
|
for _ in range(20)
|
|
]
|
|
|
|
if iter == 0:
|
|
optim = Eve(m.parameters(), lr=0.003)
|
|
elif iter == 1:
|
|
optim = ScaledAdam(m.parameters(), lr=0.03, clipping_scale=2.0)
|
|
scheduler = Eden(optim, lr_batches=200, lr_epochs=5, verbose=False)
|
|
|
|
start = timeit.default_timer()
|
|
avg_loss = 0.0
|
|
for epoch in range(180):
|
|
scheduler.step_epoch()
|
|
# if epoch == 100 and iter in [2,3]:
|
|
# optim.reset_speedup() # check it doesn't crash.
|
|
|
|
# if epoch == 130:
|
|
# opts = diagnostics.TensorDiagnosticOptions(
|
|
# 2 ** 22
|
|
# ) # allow 4 megabytes per sub-module
|
|
# diagnostic = diagnostics.attach_diagnostics(m, opts)
|
|
|
|
for n, (x, y) in enumerate(train_pairs):
|
|
y_out = m(x)
|
|
loss = ((y_out - y) ** 2).mean() * 100.0
|
|
if epoch == 0 and n == 0:
|
|
avg_loss = loss.item()
|
|
else:
|
|
avg_loss = 0.98 * avg_loss + 0.02 * loss.item()
|
|
if n == 0 and epoch % 5 == 0:
|
|
# norm1 = '%.2e' % (m[0].weight**2).mean().sqrt().item()
|
|
# norm1b = '%.2e' % (m[0].bias**2).mean().sqrt().item()
|
|
# norm2 = '%.2e' % (m[2].weight**2).mean().sqrt().item()
|
|
# norm2b = '%.2e' % (m[2].bias**2).mean().sqrt().item()
|
|
# scale1 = '%.2e' % (m[0].weight_scale.exp().item())
|
|
# scale1b = '%.2e' % (m[0].bias_scale.exp().item())
|
|
# scale2 = '%.2e' % (m[2].weight_scale.exp().item())
|
|
# scale2b = '%.2e' % (m[2].bias_scale.exp().item())
|
|
lr = scheduler.get_last_lr()[0]
|
|
logging.info(
|
|
f"Iter {iter}, epoch {epoch}, batch {n}, avg_loss {avg_loss:.4g}, lr={lr:.4e}"
|
|
) # , norms={norm1,norm1b,norm2,norm2b}") # scales={scale1,scale1b,scale2,scale2b}
|
|
loss.log().backward()
|
|
optim.step()
|
|
optim.zero_grad()
|
|
scheduler.step_batch()
|
|
|
|
# diagnostic.print_diagnostics()
|
|
|
|
stop = timeit.default_timer()
|
|
logging.info(f"Iter={iter}, Time taken: {stop - start}")
|
|
|
|
logging.info(f"last lr = {scheduler.get_last_lr()}")
|
|
# logging.info("state dict = ", scheduler.state_dict())
|
|
# logging.info("optim state_dict = ", optim.state_dict())
|
|
logging.info(f"input_magnitudes = {input_magnitudes}")
|
|
logging.info(f"output_magnitudes = {output_magnitudes}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
torch.set_num_threads(1)
|
|
torch.set_num_interop_threads(1)
|
|
logging.getLogger().setLevel(logging.INFO)
|
|
import subprocess
|
|
|
|
s = subprocess.check_output(
|
|
"git status -uno .; git log -1; git diff HEAD .", shell=True
|
|
)
|
|
logging.info(s)
|
|
import sys
|
|
|
|
if len(sys.argv) > 1:
|
|
hidden_dim = int(sys.argv[1])
|
|
else:
|
|
hidden_dim = 200
|
|
|
|
_test_scaled_adam(hidden_dim)
|
|
_test_eden()
|