Module audiocraft.models.unet

Pytorch Unet Module used for diffusion.

Functions

def get_model(cfg, channels: int, side: int, num_steps: int)

Expand source code

def get_model(cfg, channels: int, side: int, num_steps: int):
    if cfg.model == 'unet':
        return DiffusionUnet(
            chin=channels, num_steps=num_steps, **cfg.diffusion_unet)
    else:
        raise RuntimeError('Not Implemented')

Classes

class BLSTM (dim, layers=2)

Expand source code

class BLSTM(nn.Module):
    """BiLSTM with same hidden units as input dim.
    """
    def __init__(self, dim, layers=2):
        super().__init__()
        self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim)
        self.linear = nn.Linear(2 * dim, dim)

    def forward(self, x):
        x = x.permute(2, 0, 1)
        x = self.lstm(x)[0]
        x = self.linear(x)
        x = x.permute(1, 2, 0)
        return x

BiLSTM with same hidden units as input dim.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

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def forward(self, x):
    x = x.permute(2, 0, 1)
    x = self.lstm(x)[0]
    x = self.linear(x)
    x = x.permute(1, 2, 0)
    return x

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.

class DecoderLayer (chin: int,
chout: int,
kernel: int = 4,
stride: int = 2,
norm_groups: int = 4,
res_blocks: int = 1,
activation: Type[torch.nn.modules.module.Module] = torch.nn.modules.activation.ReLU,
dropout: float = 0.0)

Expand source code

class DecoderLayer(nn.Module):
    def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
                 norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
                 dropout: float = 0.):
        super().__init__()
        padding = (kernel - stride) // 2
        self.res_blocks = nn.Sequential(
            *[ResBlock(chin, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
              for idx in range(res_blocks)])
        self.norm = nn.GroupNorm(norm_groups, chin)
        ConvTr = nn.ConvTranspose1d
        self.convtr = ConvTr(chin, chout, kernel, stride, padding, bias=False)
        self.activation = activation()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.res_blocks(x)
        x = self.norm(x)
        x = self.activation(x)
        x = self.convtr(x)
        return x

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self, x: torch.Tensor) ‑> torch.Tensor

Expand source code

def forward(self, x: torch.Tensor) -> torch.Tensor:
    x = self.res_blocks(x)
    x = self.norm(x)
    x = self.activation(x)
    x = self.convtr(x)
    return x

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.

class DiffusionUnet (chin: int = 3,
hidden: int = 24,
depth: int = 3,
growth: float = 2.0,
max_channels: int = 10000,
num_steps: int = 1000,
emb_all_layers=False,
cross_attention: bool = False,
bilstm: bool = False,
transformer: bool = False,
codec_dim: int | None = None,
**kwargs)

Expand source code

class DiffusionUnet(nn.Module):
    def __init__(self, chin: int = 3, hidden: int = 24, depth: int = 3, growth: float = 2.,
                 max_channels: int = 10_000, num_steps: int = 1000, emb_all_layers=False, cross_attention: bool = False,
                 bilstm: bool = False, transformer: bool = False,
                 codec_dim: tp.Optional[int] = None, **kwargs):
        super().__init__()
        self.encoders = nn.ModuleList()
        self.decoders = nn.ModuleList()
        self.embeddings: tp.Optional[nn.ModuleList] = None
        self.embedding = nn.Embedding(num_steps, hidden)
        if emb_all_layers:
            self.embeddings = nn.ModuleList()
        self.condition_embedding: tp.Optional[nn.Module] = None
        for d in range(depth):
            encoder = EncoderLayer(chin, hidden, **kwargs)
            decoder = DecoderLayer(hidden, chin, **kwargs)
            self.encoders.append(encoder)
            self.decoders.insert(0, decoder)
            if emb_all_layers and d > 0:
                assert self.embeddings is not None
                self.embeddings.append(nn.Embedding(num_steps, hidden))
            chin = hidden
            hidden = min(int(chin * growth), max_channels)
        self.bilstm: tp.Optional[nn.Module]
        if bilstm:
            self.bilstm = BLSTM(chin)
        else:
            self.bilstm = None
        self.use_transformer = transformer
        self.cross_attention = False
        if transformer:
            self.cross_attention = cross_attention
            self.transformer = StreamingTransformer(chin, 8, 6, bias_ff=False, bias_attn=False,
                                                    cross_attention=cross_attention)

        self.use_codec = False
        if codec_dim is not None:
            self.conv_codec = nn.Conv1d(codec_dim, chin, 1)
            self.use_codec = True

    def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
        skips = []
        bs = x.size(0)
        z = x
        view_args = [1]
        if type(step) is torch.Tensor:
            step_tensor = step
        else:
            step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)

        for idx, encoder in enumerate(self.encoders):
            z = encoder(z)
            if idx == 0:
                z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
            elif self.embeddings is not None:
                z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)

            skips.append(z)

        if self.use_codec:  # insert condition in the bottleneck
            assert condition is not None, "Model defined for conditionnal generation"
            condition_emb = self.conv_codec(condition)  # reshape to the bottleneck dim
            assert condition_emb.size(-1) <= 2 * z.size(-1), \
                f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
            if not self.cross_attention:

                condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
                assert z.size() == condition_emb.size()
                z += condition_emb
                cross_attention_src = None
            else:
                cross_attention_src = condition_emb.permute(0, 2, 1)  # B, T, C
                B, T, C = cross_attention_src.shape
                positions = torch.arange(T, device=x.device).view(1, -1, 1)
                pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
                cross_attention_src = cross_attention_src + pos_emb
        if self.use_transformer:
            z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
        else:
            if self.bilstm is None:
                z = torch.zeros_like(z)
            else:
                z = self.bilstm(z)

        for decoder in self.decoders:
            s = skips.pop(-1)
            z = z[:, :, :s.shape[2]]
            z = z + s
            z = decoder(z)

        z = z[:, :, :x.shape[2]]
        return Output(z)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self,
x: torch.Tensor,
step: int | torch.Tensor,
condition: torch.Tensor | None = None) ‑> Callable[..., Any]

Expand source code

def forward(self, x: torch.Tensor, step: tp.Union[int, torch.Tensor], condition: tp.Optional[torch.Tensor] = None):
    skips = []
    bs = x.size(0)
    z = x
    view_args = [1]
    if type(step) is torch.Tensor:
        step_tensor = step
    else:
        step_tensor = torch.tensor([step], device=x.device, dtype=torch.long).expand(bs)

    for idx, encoder in enumerate(self.encoders):
        z = encoder(z)
        if idx == 0:
            z = z + self.embedding(step_tensor).view(bs, -1, *view_args).expand_as(z)
        elif self.embeddings is not None:
            z = z + self.embeddings[idx - 1](step_tensor).view(bs, -1, *view_args).expand_as(z)

        skips.append(z)

    if self.use_codec:  # insert condition in the bottleneck
        assert condition is not None, "Model defined for conditionnal generation"
        condition_emb = self.conv_codec(condition)  # reshape to the bottleneck dim
        assert condition_emb.size(-1) <= 2 * z.size(-1), \
            f"You are downsampling the conditionning with factor >=2 : {condition_emb.size(-1)=} and {z.size(-1)=}"
        if not self.cross_attention:

            condition_emb = torch.nn.functional.interpolate(condition_emb, z.size(-1))
            assert z.size() == condition_emb.size()
            z += condition_emb
            cross_attention_src = None
        else:
            cross_attention_src = condition_emb.permute(0, 2, 1)  # B, T, C
            B, T, C = cross_attention_src.shape
            positions = torch.arange(T, device=x.device).view(1, -1, 1)
            pos_emb = create_sin_embedding(positions, C, max_period=10_000, dtype=cross_attention_src.dtype)
            cross_attention_src = cross_attention_src + pos_emb
    if self.use_transformer:
        z = self.transformer(z.permute(0, 2, 1), cross_attention_src=cross_attention_src).permute(0, 2, 1)
    else:
        if self.bilstm is None:
            z = torch.zeros_like(z)
        else:
            z = self.bilstm(z)

    for decoder in self.decoders:
        s = skips.pop(-1)
        z = z[:, :, :s.shape[2]]
        z = z + s
        z = decoder(z)

    z = z[:, :, :x.shape[2]]
    return Output(z)

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.

class EncoderLayer (chin: int,
chout: int,
kernel: int = 4,
stride: int = 2,
norm_groups: int = 4,
res_blocks: int = 1,
activation: Type[torch.nn.modules.module.Module] = torch.nn.modules.activation.ReLU,
dropout: float = 0.0)

Expand source code

class EncoderLayer(nn.Module):
    def __init__(self, chin: int, chout: int, kernel: int = 4, stride: int = 2,
                 norm_groups: int = 4, res_blocks: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
                 dropout: float = 0.):
        super().__init__()
        padding = (kernel - stride) // 2
        Conv = nn.Conv1d
        self.conv = Conv(chin, chout, kernel, stride, padding, bias=False)
        self.norm = nn.GroupNorm(norm_groups, chout)
        self.activation = activation()
        self.res_blocks = nn.Sequential(
            *[ResBlock(chout, norm_groups=norm_groups, dilation=2**idx, dropout=dropout)
              for idx in range(res_blocks)])

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, C, T = x.shape
        stride, = self.conv.stride
        pad = (stride - (T % stride)) % stride
        x = F.pad(x, (0, pad))

        x = self.conv(x)
        x = self.norm(x)
        x = self.activation(x)
        x = self.res_blocks(x)
        return x

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self, x: torch.Tensor) ‑> torch.Tensor

Expand source code

def forward(self, x: torch.Tensor) -> torch.Tensor:
    B, C, T = x.shape
    stride, = self.conv.stride
    pad = (stride - (T % stride)) % stride
    x = F.pad(x, (0, pad))

    x = self.conv(x)
    x = self.norm(x)
    x = self.activation(x)
    x = self.res_blocks(x)
    return x

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.

class Output (sample: torch.Tensor)

Expand source code

@dataclass
class Output:
    sample: torch.Tensor

Output(sample: torch.Tensor)

Class variables

var sample : torch.Tensor

class ResBlock (channels: int,
kernel: int = 3,
norm_groups: int = 4,
dilation: int = 1,
activation: Type[torch.nn.modules.module.Module] = torch.nn.modules.activation.ReLU,
dropout: float = 0.0)

Expand source code

class ResBlock(nn.Module):
    def __init__(self, channels: int, kernel: int = 3, norm_groups: int = 4,
                 dilation: int = 1, activation: tp.Type[nn.Module] = nn.ReLU,
                 dropout: float = 0.):
        super().__init__()
        stride = 1
        padding = dilation * (kernel - stride) // 2
        Conv = nn.Conv1d
        Drop = nn.Dropout1d
        self.norm1 = nn.GroupNorm(norm_groups, channels)
        self.conv1 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
        self.activation1 = activation()
        self.dropout1 = Drop(dropout)

        self.norm2 = nn.GroupNorm(norm_groups, channels)
        self.conv2 = Conv(channels, channels, kernel, 1, padding, dilation=dilation)
        self.activation2 = activation()
        self.dropout2 = Drop(dropout)

    def forward(self, x):
        h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
        h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
        return x + h

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

Expand source code

def forward(self, x):
    h = self.dropout1(self.conv1(self.activation1(self.norm1(x))))
    h = self.dropout2(self.conv2(self.activation2(self.norm2(h))))
    return x + h

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.