facebookresearch--audiocraft
Alexandre Défossez (autodoc)
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<header>
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<h1 class="title">Module <code>audiocraft.optim.fsdp</code></h1>
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</header>
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<section id="section-intro">
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<p>Wrapper around FSDP for more convenient use in the training loops.</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.optim.fsdp.is_fsdp_used"><code class="name flex">
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<span>def <span class="ident">is_fsdp_used</span></span>(<span>) ‑> bool</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 is_fsdp_used() -> bool:
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"""Return whether we are using FSDP."""
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# A bit of a hack but should work from anywhere.
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if dora.is_xp():
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cfg = dora.get_xp().cfg
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if hasattr(cfg, 'fsdp'):
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return cfg.fsdp.use
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return False</code></pre>
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</details>
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<div class="desc"><p>Return whether we are using FSDP.</p></div>
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</dd>
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<dt id="audiocraft.optim.fsdp.is_sharded_tensor"><code class="name flex">
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<span>def <span class="ident">is_sharded_tensor</span></span>(<span>x: Any) ‑> bool</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 is_sharded_tensor(x: tp.Any) -> bool:
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return isinstance(x, ShardedTensor)</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.optim.fsdp.purge_fsdp"><code class="name flex">
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<span>def <span class="ident">purge_fsdp</span></span>(<span>model: torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel)</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 purge_fsdp(model: FSDP):
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"""Purge the FSDP cached shard inside the model. This should
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allow setting the best state or switching to the EMA.
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"""
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from torch.distributed.fsdp._runtime_utils import _reshard # type: ignore
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for module in FSDP.fsdp_modules(model):
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if hasattr(module, "_handles"):
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# support for FSDP with torch<2.1.0
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handles = module._handles
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if not handles:
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continue
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handle = handles[0]
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unsharded_flat_param = handle._get_padded_unsharded_flat_param()
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storage_size: int = unsharded_flat_param._typed_storage()._size() # type: ignore
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if storage_size == 0:
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continue
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true_list = [True for h in handles]
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_reshard(module, handles, true_list)
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else:
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handle = module._handle
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if not handle:
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continue
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unsharded_flat_param = handle._get_padded_unsharded_flat_param()
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storage_size: int = unsharded_flat_param._typed_storage()._size() # type: ignore
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if storage_size == 0:
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continue
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_reshard(module, handle, True)</code></pre>
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</details>
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<div class="desc"><p>Purge the FSDP cached shard inside the model. This should
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allow setting the best state or switching to the EMA.</p></div>
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</dd>
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<dt id="audiocraft.optim.fsdp.switch_to_full_state_dict"><code class="name flex">
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<span>def <span class="ident">switch_to_full_state_dict</span></span>(<span>models: List[torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel])</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">@contextmanager
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def switch_to_full_state_dict(models: tp.List[FSDP]):
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# Another bug in FSDP makes it that we cannot use the `state_dict_type` API,
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# so let's do thing manually.
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for model in models:
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FSDP.set_state_dict_type( # type: ignore
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model, StateDictType.FULL_STATE_DICT,
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FullStateDictConfig(offload_to_cpu=True, rank0_only=True))
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try:
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yield
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finally:
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for model in models:
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FSDP.set_state_dict_type(model, StateDictType.LOCAL_STATE_DICT) # type: ignore</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.optim.fsdp.wrap_with_fsdp"><code class="name flex">
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<span>def <span class="ident">wrap_with_fsdp</span></span>(<span>cfg,<br>model: torch.nn.modules.module.Module,<br>block_classes: Set[Type] | None = None) ‑> torch.distributed.fsdp.fully_sharded_data_parallel.FullyShardedDataParallel</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 wrap_with_fsdp(cfg, model: torch.nn.Module,
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block_classes: tp.Optional[tp.Set[tp.Type]] = None) -> FSDP:
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"""Wraps a model with FSDP."""
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# Some of the typing is disabled until this gets integrated
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# into the stable version of PyTorch.
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from torch.distributed.fsdp.wrap import ModuleWrapPolicy # type: ignore
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# we import this here to prevent circular import.
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from ..modules.transformer import StreamingTransformerLayer
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from ..modules.conditioners import ConditioningProvider
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_fix_post_backward_hook()
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assert cfg.use
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sharding_strategy_dict = {
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"no_shard": ShardingStrategy.NO_SHARD,
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"shard_grad_op": ShardingStrategy.SHARD_GRAD_OP,
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"full_shard": ShardingStrategy.FULL_SHARD,
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}
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dtype_dict = {
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"float32": torch.float32,
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"float16": torch.float16,
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"bfloat16": torch.bfloat16,
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}
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mixed_precision_config = MixedPrecision(
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param_dtype=dtype_dict[cfg.param_dtype],
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reduce_dtype=dtype_dict[cfg.reduce_dtype],
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buffer_dtype=dtype_dict[cfg.buffer_dtype],
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)
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sharding_strategy_config = sharding_strategy_dict[cfg.sharding_strategy]
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# The following is going to require being a bit smart
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# when doing LM, because this would flush the weights for every time step
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# during generation. One possiblity is to use hybrid sharding:
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# See: https://pytorch.org/docs/master/fsdp.html#torch.distributed.fsdp.ShardingStrategy
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assert sharding_strategy_config != ShardingStrategy.FULL_SHARD, \
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"Not supported at the moment, requires a bit more work."
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local_rank = dora.distrib.get_distrib_spec().local_rank
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assert local_rank < torch.cuda.device_count(), "Please upgrade Dora!"
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auto_wrap_policy = None
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if block_classes is None:
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block_classes = {StreamingTransformerLayer, ConditioningProvider}
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if cfg.per_block:
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auto_wrap_policy = ModuleWrapPolicy(block_classes)
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wrapped = _FSDPFixStateDict(
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model,
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sharding_strategy=sharding_strategy_config,
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mixed_precision=mixed_precision_config,
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device_id=local_rank,
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sync_module_states=True,
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use_orig_params=True,
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auto_wrap_policy=auto_wrap_policy,
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) # type: ignore
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FSDP.set_state_dict_type(wrapped, StateDictType.LOCAL_STATE_DICT) # type: ignore
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# Let the wrapped model know about the wrapping!
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# We use __dict__ to avoid it going into the state dict.
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# This is a bit dirty, but needed during generation, as otherwise
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# the wrapped model would call itself and bypass FSDP.
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for module in FSDP.fsdp_modules(wrapped):
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original = module._fsdp_wrapped_module
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original.__dict__['_fsdp'] = module
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return wrapped</code></pre>
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</details>
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<div class="desc"><p>Wraps a model with FSDP.</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 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.optim" href="index.html">audiocraft.optim</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.optim.fsdp.is_fsdp_used" href="#audiocraft.optim.fsdp.is_fsdp_used">is_fsdp_used</a></code></li>
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<li><code><a title="audiocraft.optim.fsdp.is_sharded_tensor" href="#audiocraft.optim.fsdp.is_sharded_tensor">is_sharded_tensor</a></code></li>
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<li><code><a title="audiocraft.optim.fsdp.purge_fsdp" href="#audiocraft.optim.fsdp.purge_fsdp">purge_fsdp</a></code></li>
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<li><code><a title="audiocraft.optim.fsdp.switch_to_full_state_dict" href="#audiocraft.optim.fsdp.switch_to_full_state_dict">switch_to_full_state_dict</a></code></li>
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<li><code><a title="audiocraft.optim.fsdp.wrap_with_fsdp" href="#audiocraft.optim.fsdp.wrap_with_fsdp">wrap_with_fsdp</a></code></li>
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</ul>
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