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| # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA CORPORATION and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA CORPORATION is strictly prohibited. | |
| """Facilities for reporting and collecting training statistics across | |
| multiple processes and devices. The interface is designed to minimize | |
| synchronization overhead as well as the amount of boilerplate in user | |
| code.""" | |
| import re | |
| import numpy as np | |
| import torch | |
| import lib.dnnlib | |
| from . import misc | |
| #---------------------------------------------------------------------------- | |
| _num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] | |
| _reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. | |
| _counter_dtype = torch.float64 # Data type to use for the internal counters. | |
| _rank = 0 # Rank of the current process. | |
| _sync_device = None # Device to use for multiprocess communication. None = single-process. | |
| _sync_called = False # Has _sync() been called yet? | |
| _counters = dict( | |
| ) # Running counters on each device, updated by report(): name => device => torch.Tensor | |
| _cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor | |
| #---------------------------------------------------------------------------- | |
| def init_multiprocessing(rank, sync_device): | |
| r"""Initializes `torch_utils.training_stats` for collecting statistics | |
| across multiple processes. | |
| This function must be called after | |
| `torch.distributed.init_process_group()` and before `Collector.update()`. | |
| The call is not necessary if multi-process collection is not needed. | |
| Args: | |
| rank: Rank of the current process. | |
| sync_device: PyTorch device to use for inter-process | |
| communication, or None to disable multi-process | |
| collection. Typically `torch.device('cuda', rank)`. | |
| """ | |
| global _rank, _sync_device | |
| assert not _sync_called | |
| _rank = rank | |
| _sync_device = sync_device | |
| #---------------------------------------------------------------------------- | |
| def report(name, value): | |
| r"""Broadcasts the given set of scalars to all interested instances of | |
| `Collector`, across device and process boundaries. | |
| This function is expected to be extremely cheap and can be safely | |
| called from anywhere in the training loop, loss function, or inside a | |
| `torch.nn.Module`. | |
| Warning: The current implementation expects the set of unique names to | |
| be consistent across processes. Please make sure that `report()` is | |
| called at least once for each unique name by each process, and in the | |
| same order. If a given process has no scalars to broadcast, it can do | |
| `report(name, [])` (empty list). | |
| Args: | |
| name: Arbitrary string specifying the name of the statistic. | |
| Averages are accumulated separately for each unique name. | |
| value: Arbitrary set of scalars. Can be a list, tuple, | |
| NumPy array, PyTorch tensor, or Python scalar. | |
| Returns: | |
| The same `value` that was passed in. | |
| """ | |
| if name not in _counters: | |
| _counters[name] = dict() | |
| elems = torch.as_tensor(value) | |
| if elems.numel() == 0: | |
| return value | |
| elems = elems.detach().flatten().to(_reduce_dtype) | |
| moments = torch.stack([ | |
| torch.ones_like(elems).sum(), | |
| elems.sum(), | |
| elems.square().sum(), | |
| ]) | |
| assert moments.ndim == 1 and moments.shape[0] == _num_moments | |
| moments = moments.to(_counter_dtype) | |
| device = moments.device | |
| if device not in _counters[name]: | |
| _counters[name][device] = torch.zeros_like(moments) | |
| _counters[name][device].add_(moments) | |
| return value | |
| #---------------------------------------------------------------------------- | |
| def report0(name, value): | |
| r"""Broadcasts the given set of scalars by the first process (`rank = 0`), | |
| but ignores any scalars provided by the other processes. | |
| See `report()` for further details. | |
| """ | |
| report(name, value if _rank == 0 else []) | |
| return value | |
| #---------------------------------------------------------------------------- | |
| class Collector: | |
| r"""Collects the scalars broadcasted by `report()` and `report0()` and | |
| computes their long-term averages (mean and standard deviation) over | |
| user-defined periods of time. | |
| The averages are first collected into internal counters that are not | |
| directly visible to the user. They are then copied to the user-visible | |
| state as a result of calling `update()` and can then be queried using | |
| `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the | |
| internal counters for the next round, so that the user-visible state | |
| effectively reflects averages collected between the last two calls to | |
| `update()`. | |
| Args: | |
| regex: Regular expression defining which statistics to | |
| collect. The default is to collect everything. | |
| keep_previous: Whether to retain the previous averages if no | |
| scalars were collected on a given round | |
| (default: True). | |
| """ | |
| def __init__(self, regex='.*', keep_previous=True): | |
| self._regex = re.compile(regex) | |
| self._keep_previous = keep_previous | |
| self._cumulative = dict() | |
| self._moments = dict() | |
| self.update() | |
| self._moments.clear() | |
| def names(self): | |
| r"""Returns the names of all statistics broadcasted so far that | |
| match the regular expression specified at construction time. | |
| """ | |
| return [name for name in _counters if self._regex.fullmatch(name)] | |
| def update(self): | |
| r"""Copies current values of the internal counters to the | |
| user-visible state and resets them for the next round. | |
| If `keep_previous=True` was specified at construction time, the | |
| operation is skipped for statistics that have received no scalars | |
| since the last update, retaining their previous averages. | |
| This method performs a number of GPU-to-CPU transfers and one | |
| `torch.distributed.all_reduce()`. It is intended to be called | |
| periodically in the main training loop, typically once every | |
| N training steps. | |
| """ | |
| if not self._keep_previous: | |
| self._moments.clear() | |
| for name, cumulative in _sync(self.names()): | |
| if name not in self._cumulative: | |
| self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) | |
| delta = cumulative - self._cumulative[name] | |
| self._cumulative[name].copy_(cumulative) | |
| if float(delta[0]) != 0: | |
| self._moments[name] = delta | |
| def _get_delta(self, name): | |
| r"""Returns the raw moments that were accumulated for the given | |
| statistic between the last two calls to `update()`, or zero if | |
| no scalars were collected. | |
| """ | |
| assert self._regex.fullmatch(name) | |
| if name not in self._moments: | |
| self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) | |
| return self._moments[name] | |
| def num(self, name): | |
| r"""Returns the number of scalars that were accumulated for the given | |
| statistic between the last two calls to `update()`, or zero if | |
| no scalars were collected. | |
| """ | |
| delta = self._get_delta(name) | |
| return int(delta[0]) | |
| def mean(self, name): | |
| r"""Returns the mean of the scalars that were accumulated for the | |
| given statistic between the last two calls to `update()`, or NaN if | |
| no scalars were collected. | |
| """ | |
| delta = self._get_delta(name) | |
| if int(delta[0]) == 0: | |
| return float('nan') | |
| return float(delta[1] / delta[0]) | |
| def std(self, name): | |
| r"""Returns the standard deviation of the scalars that were | |
| accumulated for the given statistic between the last two calls to | |
| `update()`, or NaN if no scalars were collected. | |
| """ | |
| delta = self._get_delta(name) | |
| if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): | |
| return float('nan') | |
| if int(delta[0]) == 1: | |
| return float(0) | |
| mean = float(delta[1] / delta[0]) | |
| raw_var = float(delta[2] / delta[0]) | |
| return np.sqrt(max(raw_var - np.square(mean), 0)) | |
| def as_dict(self): | |
| r"""Returns the averages accumulated between the last two calls to | |
| `update()` as an `dnnlib.EasyDict`. The contents are as follows: | |
| dnnlib.EasyDict( | |
| NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), | |
| ... | |
| ) | |
| """ | |
| stats = dnnlib.EasyDict() | |
| for name in self.names(): | |
| stats[name] = dnnlib.EasyDict( | |
| num=self.num(name), mean=self.mean(name), std=self.std(name) | |
| ) | |
| return stats | |
| def __getitem__(self, name): | |
| r"""Convenience getter. | |
| `collector[name]` is a synonym for `collector.mean(name)`. | |
| """ | |
| return self.mean(name) | |
| #---------------------------------------------------------------------------- | |
| def _sync(names): | |
| r"""Synchronize the global cumulative counters across devices and | |
| processes. Called internally by `Collector.update()`. | |
| """ | |
| if len(names) == 0: | |
| return [] | |
| global _sync_called | |
| _sync_called = True | |
| # Collect deltas within current rank. | |
| deltas = [] | |
| device = _sync_device if _sync_device is not None else torch.device('cpu') | |
| for name in names: | |
| delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) | |
| for counter in _counters[name].values(): | |
| delta.add_(counter.to(device)) | |
| counter.copy_(torch.zeros_like(counter)) | |
| deltas.append(delta) | |
| deltas = torch.stack(deltas) | |
| # Sum deltas across ranks. | |
| if _sync_device is not None: | |
| torch.distributed.all_reduce(deltas) | |
| # Update cumulative values. | |
| deltas = deltas.cpu() | |
| for idx, name in enumerate(names): | |
| if name not in _cumulative: | |
| _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) | |
| _cumulative[name].add_(deltas[idx]) | |
| # Return name-value pairs. | |
| return [(name, _cumulative[name]) for name in names] | |
| #---------------------------------------------------------------------------- | |