facebookresearch--audiocraft
Alexandre Défossez (autodoc)
484 行
26 KiB
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484 行
26 KiB
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<article id="content">
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<header>
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<h1 class="title">Module <code>audiocraft.modules.rope</code></h1>
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</header>
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<section id="section-intro">
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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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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="audiocraft.modules.rope.RotaryEmbedding"><code class="flex name class">
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<span>class <span class="ident">RotaryEmbedding</span></span>
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<span>(</span><span>dim: int,<br>max_period: float = 10000.0,<br>xpos: bool = False,<br>scale: float = 1.0,<br>device=None,<br>dtype: torch.dtype = torch.float32)</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">class RotaryEmbedding(nn.Module):
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"""Rotary positional embedding (RoPE) from [Su et al 2022](https://arxiv.org/abs/2104.09864).
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Args:
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dim (int): Embedding dimension (twice the number of frequencies).
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max_period (float): Maximum period of the rotation frequencies.
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xpos (bool): Use xPos, applies an exponential decay to rotation matrix.
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scale (float): Scale of positional embedding, set to 0 to deactivate.
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device (torch.device, optional): Device on which to initialize the module.
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dtype (torch.dtype): dtype to use to generate the embedding.
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"""
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def __init__(self, dim: int, max_period: float = 10000.0, xpos: bool = False,
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scale: float = 1.0, device=None, dtype: torch.dtype = torch.float32):
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super().__init__()
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assert dim % 2 == 0
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self.scale = scale
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assert dtype in [torch.float64, torch.float32]
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self.dtype = dtype
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adim = torch.arange(0, dim, 2, device=device, dtype=dtype)[: (dim // 2)]
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frequencies = 1.0 / (max_period ** (adim / dim))
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self.register_buffer("frequencies", frequencies)
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self.rotation: tp.Optional[torch.Tensor] = None
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self.xpos = XPos(dim, device=device, dtype=dtype) if xpos else None
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def get_rotation(self, start: int, end: int):
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"""Create complex rotation tensor, cache values for fast computation."""
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if self.rotation is None or end > self.rotation.shape[0]:
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assert isinstance(self.frequencies, torch.Tensor) # Satisfy type checker.
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idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
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angles = torch.outer(idx, self.frequencies)
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self.rotation = torch.polar(torch.ones_like(angles), angles)
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return self.rotation[start:end]
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def rotate(self, x: torch.Tensor, start: int = 0, time_dim: int = 1, invert_decay: bool = False):
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"""Apply rope rotation to query or key tensor."""
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T = x.shape[time_dim]
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target_shape = [1] * x.dim()
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target_shape[time_dim] = T
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target_shape[-1] = -1
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rotation = self.get_rotation(start, start + T).view(target_shape)
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if self.xpos:
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decay = self.xpos.get_decay(start, start + T).view(target_shape)
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else:
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decay = 1.0
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if invert_decay:
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decay = decay ** -1
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x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
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scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
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x_out = torch.view_as_real(x_complex * scaled_rotation).view_as(x)
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return x_out.type_as(x)
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def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0, time_dim: int = 1):
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""" Apply rope rotation to both query and key tensors.
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Supports streaming mode, in which query and key are not expected to have the same shape.
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In streaming mode, key will be of length [P + C] with P the cached past timesteps, but
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query will be [C] (typically C == 1).
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Args:
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query (torch.Tensor): Query to rotate.
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key (torch.Tensor): Key to rotate.
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start (int): Start index of the sequence for time offset.
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time_dim (int): which dimension represent the time steps.
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"""
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query_timesteps = query.shape[time_dim]
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key_timesteps = key.shape[time_dim]
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streaming_offset = key_timesteps - query_timesteps
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query_out = self.rotate(query, start + streaming_offset, time_dim)
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key_out = self.rotate(key, start, time_dim, invert_decay=True)
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return query_out, key_out</code></pre>
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</details>
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<div class="desc"><p>Rotary positional embedding (RoPE) from <a href="https://arxiv.org/abs/2104.09864">Su et al 2022</a>.</p>
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<h2 id="args">Args</h2>
|
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<dl>
|
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<dt><strong><code>dim</code></strong> : <code>int</code></dt>
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<dd>Embedding dimension (twice the number of frequencies).</dd>
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||
<dt><strong><code>max_period</code></strong> : <code>float</code></dt>
|
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<dd>Maximum period of the rotation frequencies.</dd>
|
||
<dt><strong><code>xpos</code></strong> : <code>bool</code></dt>
|
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<dd>Use xPos, applies an exponential decay to rotation matrix.</dd>
|
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<dt><strong><code>scale</code></strong> : <code>float</code></dt>
|
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<dd>Scale of positional embedding, set to 0 to deactivate.</dd>
|
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<dt><strong><code>device</code></strong> : <code>torch.device</code>, optional</dt>
|
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<dd>Device on which to initialize the module.</dd>
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<dt><strong><code>dtype</code></strong> : <code>torch.dtype</code></dt>
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<dd>dtype to use to generate the embedding.</dd>
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</dl>
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<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
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<h3>Ancestors</h3>
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<ul class="hlist">
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<li>torch.nn.modules.module.Module</li>
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</ul>
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<h3>Class variables</h3>
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<dl>
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<dt id="audiocraft.modules.rope.RotaryEmbedding.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="audiocraft.modules.rope.RotaryEmbedding.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="audiocraft.modules.rope.RotaryEmbedding.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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</dl>
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<h3>Methods</h3>
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<dl>
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<dt id="audiocraft.modules.rope.RotaryEmbedding.forward"><code class="name flex">
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<span>def <span class="ident">forward</span></span>(<span>self, *input: Any) ‑> None</span>
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</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def _forward_unimplemented(self, *input: Any) -> None:
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r"""Defines the computation performed at every call.
|
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Should be overridden by all subclasses.
|
||
|
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.. note::
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||
Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:`Module` instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.
|
||
"""
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raise NotImplementedError(f"Module [{type(self).__name__}] is missing the required \"forward\" function")</code></pre>
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||
</details>
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||
<div class="desc"><p>Defines the computation performed at every call.</p>
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||
<p>Should be overridden by all subclasses.</p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
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||
</div></div>
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</dd>
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<dt id="audiocraft.modules.rope.RotaryEmbedding.get_rotation"><code class="name flex">
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<span>def <span class="ident">get_rotation</span></span>(<span>self, start: int, end: int)</span>
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||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def get_rotation(self, start: int, end: int):
|
||
"""Create complex rotation tensor, cache values for fast computation."""
|
||
if self.rotation is None or end > self.rotation.shape[0]:
|
||
assert isinstance(self.frequencies, torch.Tensor) # Satisfy type checker.
|
||
idx = torch.arange(end, device=self.frequencies.device, dtype=self.dtype)
|
||
angles = torch.outer(idx, self.frequencies)
|
||
self.rotation = torch.polar(torch.ones_like(angles), angles)
|
||
return self.rotation[start:end]</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Create complex rotation tensor, cache values for fast computation.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.RotaryEmbedding.rotate"><code class="name flex">
|
||
<span>def <span class="ident">rotate</span></span>(<span>self,<br>x: torch.Tensor,<br>start: int = 0,<br>time_dim: int = 1,<br>invert_decay: bool = False)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def rotate(self, x: torch.Tensor, start: int = 0, time_dim: int = 1, invert_decay: bool = False):
|
||
"""Apply rope rotation to query or key tensor."""
|
||
T = x.shape[time_dim]
|
||
target_shape = [1] * x.dim()
|
||
target_shape[time_dim] = T
|
||
target_shape[-1] = -1
|
||
rotation = self.get_rotation(start, start + T).view(target_shape)
|
||
|
||
if self.xpos:
|
||
decay = self.xpos.get_decay(start, start + T).view(target_shape)
|
||
else:
|
||
decay = 1.0
|
||
|
||
if invert_decay:
|
||
decay = decay ** -1
|
||
|
||
x_complex = torch.view_as_complex(x.to(self.dtype).reshape(*x.shape[:-1], -1, 2))
|
||
scaled_rotation = (rotation * decay) * self.scale + (1.0 - self.scale)
|
||
x_out = torch.view_as_real(x_complex * scaled_rotation).view_as(x)
|
||
|
||
return x_out.type_as(x)</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Apply rope rotation to query or key tensor.</p></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.RotaryEmbedding.rotate_qk"><code class="name flex">
|
||
<span>def <span class="ident">rotate_qk</span></span>(<span>self, query: torch.Tensor, key: torch.Tensor, start: int = 0, time_dim: int = 1)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def rotate_qk(self, query: torch.Tensor, key: torch.Tensor, start: int = 0, time_dim: int = 1):
|
||
""" Apply rope rotation to both query and key tensors.
|
||
Supports streaming mode, in which query and key are not expected to have the same shape.
|
||
In streaming mode, key will be of length [P + C] with P the cached past timesteps, but
|
||
query will be [C] (typically C == 1).
|
||
|
||
Args:
|
||
query (torch.Tensor): Query to rotate.
|
||
key (torch.Tensor): Key to rotate.
|
||
start (int): Start index of the sequence for time offset.
|
||
time_dim (int): which dimension represent the time steps.
|
||
"""
|
||
query_timesteps = query.shape[time_dim]
|
||
key_timesteps = key.shape[time_dim]
|
||
streaming_offset = key_timesteps - query_timesteps
|
||
|
||
query_out = self.rotate(query, start + streaming_offset, time_dim)
|
||
key_out = self.rotate(key, start, time_dim, invert_decay=True)
|
||
|
||
return query_out, key_out</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Apply rope rotation to both query and key tensors.
|
||
Supports streaming mode, in which query and key are not expected to have the same shape.
|
||
In streaming mode, key will be of length [P + C] with P the cached past timesteps, but
|
||
query will be [C] (typically C == 1).</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>query</code></strong> : <code>torch.Tensor</code></dt>
|
||
<dd>Query to rotate.</dd>
|
||
<dt><strong><code>key</code></strong> : <code>torch.Tensor</code></dt>
|
||
<dd>Key to rotate.</dd>
|
||
<dt><strong><code>start</code></strong> : <code>int</code></dt>
|
||
<dd>Start index of the sequence for time offset.</dd>
|
||
<dt><strong><code>time_dim</code></strong> : <code>int</code></dt>
|
||
<dd>which dimension represent the time steps.</dd>
|
||
</dl></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.XPos"><code class="flex name class">
|
||
<span>class <span class="ident">XPos</span></span>
|
||
<span>(</span><span>dim: int,<br>smoothing: float = 0.4,<br>base_scale: int = 512,<br>device=None,<br>dtype: torch.dtype = torch.float32)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">class XPos(nn.Module):
|
||
"""Length-extrapolatable positional embedding (xPos) from [Sun et al 2022](https://arxiv.org/abs/2212.10554v1).
|
||
This applies an exponential decay to the RoPE rotation matrix.
|
||
|
||
Args:
|
||
dim (int): Embedding dimension.
|
||
smoothing (float): Smoothing factor applied to the decay rates.
|
||
base_scale (int): Base decay rate, given in terms of scaling time.
|
||
device (torch.device, optional): Device on which to initialize the module.
|
||
dtype (torch.dtype): dtype to use to generate the embedding.
|
||
"""
|
||
def __init__(self, dim: int, smoothing: float = 0.4, base_scale: int = 512,
|
||
device=None, dtype: torch.dtype = torch.float32):
|
||
super().__init__()
|
||
assert dim % 2 == 0
|
||
assert dtype in [torch.float64, torch.float32]
|
||
self.dtype = dtype
|
||
self.base_scale = base_scale
|
||
|
||
half_dim = dim // 2
|
||
adim = torch.arange(half_dim, device=device, dtype=dtype)
|
||
decay_rates = (adim / half_dim + smoothing) / (1.0 + smoothing)
|
||
self.register_buffer("decay_rates", decay_rates)
|
||
self.decay: tp.Optional[torch.Tensor] = None
|
||
|
||
def get_decay(self, start: int, end: int):
|
||
"""Create complex decay tensor, cache values for fast computation."""
|
||
if self.decay is None or end > self.decay.shape[0]:
|
||
assert isinstance(self.decay_rates, torch.Tensor) # Satisfy type checker.
|
||
idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
|
||
power = idx / self.base_scale
|
||
scale = self.decay_rates ** power.unsqueeze(-1)
|
||
self.decay = torch.polar(scale, torch.zeros_like(scale))
|
||
return self.decay[start:end] # [T, C/2]</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Length-extrapolatable positional embedding (xPos) from <a href="https://arxiv.org/abs/2212.10554v1">Sun et al 2022</a>.
|
||
This applies an exponential decay to the RoPE rotation matrix.</p>
|
||
<h2 id="args">Args</h2>
|
||
<dl>
|
||
<dt><strong><code>dim</code></strong> : <code>int</code></dt>
|
||
<dd>Embedding dimension.</dd>
|
||
<dt><strong><code>smoothing</code></strong> : <code>float</code></dt>
|
||
<dd>Smoothing factor applied to the decay rates.</dd>
|
||
<dt><strong><code>base_scale</code></strong> : <code>int</code></dt>
|
||
<dd>Base decay rate, given in terms of scaling time.</dd>
|
||
<dt><strong><code>device</code></strong> : <code>torch.device</code>, optional</dt>
|
||
<dd>Device on which to initialize the module.</dd>
|
||
<dt><strong><code>dtype</code></strong> : <code>torch.dtype</code></dt>
|
||
<dd>dtype to use to generate the embedding.</dd>
|
||
</dl>
|
||
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
|
||
<h3>Ancestors</h3>
|
||
<ul class="hlist">
|
||
<li>torch.nn.modules.module.Module</li>
|
||
</ul>
|
||
<h3>Class variables</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.rope.XPos.call_super_init"><code class="name">var <span class="ident">call_super_init</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.XPos.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.XPos.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
|
||
<dd>
|
||
<div class="desc"></div>
|
||
</dd>
|
||
</dl>
|
||
<h3>Methods</h3>
|
||
<dl>
|
||
<dt id="audiocraft.modules.rope.XPos.forward"><code class="name flex">
|
||
<span>def <span class="ident">forward</span></span>(<span>self, *input: Any) ‑> None</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def _forward_unimplemented(self, *input: Any) -> None:
|
||
r"""Defines the computation performed at every call.
|
||
|
||
Should be overridden by all subclasses.
|
||
|
||
.. note::
|
||
Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:`Module` instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.
|
||
"""
|
||
raise NotImplementedError(f"Module [{type(self).__name__}] is missing the required \"forward\" function")</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Defines the computation performed at every call.</p>
|
||
<p>Should be overridden by all subclasses.</p>
|
||
<div class="admonition note">
|
||
<p class="admonition-title">Note</p>
|
||
<p>Although the recipe for forward pass needs to be defined within
|
||
this function, one should call the :class:<code>Module</code> instance afterwards
|
||
instead of this since the former takes care of running the
|
||
registered hooks while the latter silently ignores them.</p>
|
||
</div></div>
|
||
</dd>
|
||
<dt id="audiocraft.modules.rope.XPos.get_decay"><code class="name flex">
|
||
<span>def <span class="ident">get_decay</span></span>(<span>self, start: int, end: int)</span>
|
||
</code></dt>
|
||
<dd>
|
||
<details class="source">
|
||
<summary>
|
||
<span>Expand source code</span>
|
||
</summary>
|
||
<pre><code class="python">def get_decay(self, start: int, end: int):
|
||
"""Create complex decay tensor, cache values for fast computation."""
|
||
if self.decay is None or end > self.decay.shape[0]:
|
||
assert isinstance(self.decay_rates, torch.Tensor) # Satisfy type checker.
|
||
idx = torch.arange(end, device=self.decay_rates.device, dtype=self.dtype)
|
||
power = idx / self.base_scale
|
||
scale = self.decay_rates ** power.unsqueeze(-1)
|
||
self.decay = torch.polar(scale, torch.zeros_like(scale))
|
||
return self.decay[start:end] # [T, C/2]</code></pre>
|
||
</details>
|
||
<div class="desc"><p>Create complex decay tensor, cache values for fast computation.</p></div>
|
||
</dd>
|
||
</dl>
|
||
</dd>
|
||
</dl>
|
||
</section>
|
||
</article>
|
||
<nav id="sidebar">
|
||
<div class="toc">
|
||
<ul></ul>
|
||
</div>
|
||
<ul id="index">
|
||
<li><h3>Super-module</h3>
|
||
<ul>
|
||
<li><code><a title="audiocraft.modules" href="index.html">audiocraft.modules</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||
<ul>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.rope.RotaryEmbedding" href="#audiocraft.modules.rope.RotaryEmbedding">RotaryEmbedding</a></code></h4>
|
||
<ul class="two-column">
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.call_super_init" href="#audiocraft.modules.rope.RotaryEmbedding.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.dump_patches" href="#audiocraft.modules.rope.RotaryEmbedding.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.forward" href="#audiocraft.modules.rope.RotaryEmbedding.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.get_rotation" href="#audiocraft.modules.rope.RotaryEmbedding.get_rotation">get_rotation</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.rotate" href="#audiocraft.modules.rope.RotaryEmbedding.rotate">rotate</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.rotate_qk" href="#audiocraft.modules.rope.RotaryEmbedding.rotate_qk">rotate_qk</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.RotaryEmbedding.training" href="#audiocraft.modules.rope.RotaryEmbedding.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
<li>
|
||
<h4><code><a title="audiocraft.modules.rope.XPos" href="#audiocraft.modules.rope.XPos">XPos</a></code></h4>
|
||
<ul class="">
|
||
<li><code><a title="audiocraft.modules.rope.XPos.call_super_init" href="#audiocraft.modules.rope.XPos.call_super_init">call_super_init</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.XPos.dump_patches" href="#audiocraft.modules.rope.XPos.dump_patches">dump_patches</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.XPos.forward" href="#audiocraft.modules.rope.XPos.forward">forward</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.XPos.get_decay" href="#audiocraft.modules.rope.XPos.get_decay">get_decay</a></code></li>
|
||
<li><code><a title="audiocraft.modules.rope.XPos.training" href="#audiocraft.modules.rope.XPos.training">training</a></code></li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</li>
|
||
</ul>
|
||
</nav>
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</main>
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<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.5</a>.</p>
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