facebookresearch--audiocraft
Alexandre Défossez (autodoc)
250 行
16 KiB
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250 行
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See more info on how to use dora: https://github.com/facebookresearch/dora">
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<article id="content">
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<header>
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<h1 class="title">Module <code>audiocraft.train</code></h1>
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</header>
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<section id="section-intro">
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<p>Entry point for dora to launch solvers for running training loops.
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See more info on how to use dora: <a href="https://github.com/facebookresearch/dora">https://github.com/facebookresearch/dora</a></p>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="audiocraft.train.get_solver"><code class="name flex">
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<span>def <span class="ident">get_solver</span></span>(<span>cfg)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def get_solver(cfg):
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from . import solvers
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# Convert batch size to batch size for each GPU
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assert cfg.dataset.batch_size % flashy.distrib.world_size() == 0
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cfg.dataset.batch_size //= flashy.distrib.world_size()
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for split in ['train', 'valid', 'evaluate', 'generate']:
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if hasattr(cfg.dataset, split) and hasattr(cfg.dataset[split], 'batch_size'):
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assert cfg.dataset[split].batch_size % flashy.distrib.world_size() == 0
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cfg.dataset[split].batch_size //= flashy.distrib.world_size()
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resolve_config_dset_paths(cfg)
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solver = solvers.get_solver(cfg)
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return solver</code></pre>
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</details>
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<div class="desc"></div>
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</dd>
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<dt id="audiocraft.train.get_solver_from_sig"><code class="name flex">
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<span>def <span class="ident">get_solver_from_sig</span></span>(<span>sig: str, *args, **kwargs)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def get_solver_from_sig(sig: str, *args, **kwargs):
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"""Return Solver object from Dora signature, i.e. to play with it from a notebook.
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See `get_solver_from_xp` for more information.
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"""
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xp = main.get_xp_from_sig(sig)
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return get_solver_from_xp(xp, *args, **kwargs)</code></pre>
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</details>
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<div class="desc"><p>Return Solver object from Dora signature, i.e. to play with it from a notebook.
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See <code><a title="audiocraft.train.get_solver_from_xp" href="#audiocraft.train.get_solver_from_xp">get_solver_from_xp()</a></code> for more information.</p></div>
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</dd>
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<dt id="audiocraft.train.get_solver_from_xp"><code class="name flex">
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<span>def <span class="ident">get_solver_from_xp</span></span>(<span>xp: dora.xp.XP,<br>override_cfg: dict | omegaconf.dictconfig.DictConfig | None = None,<br>restore: bool = True,<br>load_best: bool = True,<br>ignore_state_keys: List[str] = [],<br>disable_fsdp: bool = True)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def get_solver_from_xp(xp: XP, override_cfg: tp.Optional[tp.Union[dict, omegaconf.DictConfig]] = None,
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restore: bool = True, load_best: bool = True,
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ignore_state_keys: tp.List[str] = [], disable_fsdp: bool = True):
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"""Given a XP, return the Solver object.
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Args:
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xp (XP): Dora experiment for which to retrieve the solver.
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override_cfg (dict or None): If not None, should be a dict used to
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override some values in the config of `xp`. This will not impact
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the XP signature or folder. The format is different
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than the one used in Dora grids, nested keys should actually be nested dicts,
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not flattened, e.g. `{'optim': {'batch_size': 32}}`.
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restore (bool): If `True` (the default), restore state from the last checkpoint.
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load_best (bool): If `True` (the default), load the best state from the checkpoint.
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ignore_state_keys (list[str]): List of sources to ignore when loading the state, e.g. `optimizer`.
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disable_fsdp (bool): if True, disables FSDP entirely. This will
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also automatically skip loading the EMA. For solver specific
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state sources, like the optimizer, you might want to
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use along `ignore_state_keys=['optimizer']`. Must be used with `load_best=True`.
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"""
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logger.info(f"Loading solver from XP {xp.sig}. "
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f"Overrides used: {xp.argv}")
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cfg = xp.cfg
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if override_cfg is not None:
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cfg = omegaconf.OmegaConf.merge(cfg, omegaconf.DictConfig(override_cfg))
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if disable_fsdp and cfg.fsdp.use:
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cfg.fsdp.use = False
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assert load_best is True
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# ignoring some keys that were FSDP sharded like model, ema, and best_state.
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# fsdp_best_state will be used in that case. When using a specific solver,
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# one is responsible for adding the relevant keys, e.g. 'optimizer'.
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# We could make something to automatically register those inside the solver, but that
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# seem overkill at this point.
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ignore_state_keys = ignore_state_keys + ['model', 'ema', 'best_state']
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try:
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with xp.enter():
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solver = get_solver(cfg)
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if restore:
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solver.restore(load_best=load_best, ignore_state_keys=ignore_state_keys)
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return solver
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finally:
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hydra.core.global_hydra.GlobalHydra.instance().clear()</code></pre>
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</details>
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<div class="desc"><p>Given a XP, return the Solver object.</p>
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<h2 id="args">Args</h2>
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<dl>
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<dt><strong><code>xp</code></strong> : <code>XP</code></dt>
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<dd>Dora experiment for which to retrieve the solver.</dd>
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<dt><strong><code>override_cfg</code></strong> : <code>dict</code> or <code>None</code></dt>
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<dd>If not None, should be a dict used to
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override some values in the config of <code>xp</code>. This will not impact
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the XP signature or folder. The format is different
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than the one used in Dora grids, nested keys should actually be nested dicts,
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not flattened, e.g. <code>{'optim': {'batch_size': 32}}</code>.</dd>
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<dt><strong><code>restore</code></strong> : <code>bool</code></dt>
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<dd>If <code>True</code> (the default), restore state from the last checkpoint.</dd>
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<dt><strong><code>load_best</code></strong> : <code>bool</code></dt>
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<dd>If <code>True</code> (the default), load the best state from the checkpoint.</dd>
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<dt><strong><code>ignore_state_keys</code></strong> : <code>list[str]</code></dt>
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<dd>List of sources to ignore when loading the state, e.g. <code>optimizer</code>.</dd>
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<dt><strong><code>disable_fsdp</code></strong> : <code>bool</code></dt>
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<dd>if True, disables FSDP entirely. This will
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also automatically skip loading the EMA. For solver specific
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state sources, like the optimizer, you might want to
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use along <code>ignore_state_keys=['optimizer']</code>. Must be used with <code>load_best=True</code>.</dd>
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</dl></div>
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</dd>
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<dt id="audiocraft.train.init_seed_and_system"><code class="name flex">
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<span>def <span class="ident">init_seed_and_system</span></span>(<span>cfg)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def init_seed_and_system(cfg):
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import numpy as np
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import torch
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import random
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from audiocraft.modules.transformer import set_efficient_attention_backend
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multiprocessing.set_start_method(cfg.mp_start_method)
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logger.debug('Setting mp start method to %s', cfg.mp_start_method)
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random.seed(cfg.seed)
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np.random.seed(cfg.seed)
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# torch also initialize cuda seed if available
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torch.manual_seed(cfg.seed)
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torch.set_num_threads(cfg.num_threads)
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os.environ['MKL_NUM_THREADS'] = str(cfg.num_threads)
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os.environ['OMP_NUM_THREADS'] = str(cfg.num_threads)
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logger.debug('Setting num threads to %d', cfg.num_threads)
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set_efficient_attention_backend(cfg.efficient_attention_backend)
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logger.debug('Setting efficient attention backend to %s', cfg.efficient_attention_backend)
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if 'SLURM_JOB_ID' in os.environ:
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tmpdir = Path('/scratch/slurm_tmpdir/' + os.environ['SLURM_JOB_ID'])
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if tmpdir.exists():
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logger.info("Changing tmpdir to %s", tmpdir)
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os.environ['TMPDIR'] = str(tmpdir)</code></pre>
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</details>
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<div class="desc"></div>
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</dd>
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<dt id="audiocraft.train.resolve_config_dset_paths"><code class="name flex">
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<span>def <span class="ident">resolve_config_dset_paths</span></span>(<span>cfg)</span>
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</code></dt>
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<dd>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">def resolve_config_dset_paths(cfg):
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"""Enable Dora to load manifest from git clone repository."""
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# manifest files for the different splits
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for key, value in cfg.datasource.items():
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if isinstance(value, str):
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cfg.datasource[key] = git_save.to_absolute_path(value)</code></pre>
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</details>
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<div class="desc"><p>Enable Dora to load manifest from git clone repository.</p></div>
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</dd>
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</dl>
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</section>
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<section>
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</section>
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<ul></ul>
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</div>
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<ul id="index">
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="audiocraft" href="index.html">audiocraft</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-functions">Functions</a></h3>
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<ul class="">
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<li><code><a title="audiocraft.train.get_solver" href="#audiocraft.train.get_solver">get_solver</a></code></li>
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<li><code><a title="audiocraft.train.get_solver_from_sig" href="#audiocraft.train.get_solver_from_sig">get_solver_from_sig</a></code></li>
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<li><code><a title="audiocraft.train.get_solver_from_xp" href="#audiocraft.train.get_solver_from_xp">get_solver_from_xp</a></code></li>
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<li><code><a title="audiocraft.train.init_seed_and_system" href="#audiocraft.train.init_seed_and_system">init_seed_and_system</a></code></li>
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<li><code><a title="audiocraft.train.resolve_config_dset_paths" href="#audiocraft.train.resolve_config_dset_paths">resolve_config_dset_paths</a></code></li>
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</ul>
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</li>
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