text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
lyric_ids = [[self.lyrics_encoder.get(character, 0) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def _tokenize(self, lyrics):
"""
Converts a string into a sequence of tokens (string), using the tokenizer. Split in words for word-based
vocabular... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
def prepare_for_tokenization(
self, artists: str, genres: str, lyrics: str, is_split_into_words: bool = False
) -> Tuple[str, str, str, Dict[str, Any]]:
"""
Performs any necessary transformations before tokenization. | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
Args:
artist (`str`):
The artist name to prepare. This will mostly lower the string
genres (`str`):
The genre name to prepare. This will mostly lower the string.
lyrics (`str`):
The lyrics to prepare.
is_split_into_words (`b... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
self._normalize(genre) + ".v2" for genre in genres[idx].split("_")
] # split is for the full dictionary with combined genres | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
if self.version[0] == "v2":
self.out_of_vocab = regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+")
vocab = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
self.vocab = {vocab[index]: index + 1 for index in range(len(vocab))}
self.vo... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
accepted = (
[chr(i) for i in range(ord("a"), ord("z") + 1)]
+ [chr(i) for i in range(ord("A"), ord("Z") + 1)]
+ [chr(i) for i in range(ord("0"), ord("9") + 1)]
+ ["."]
)
accepted = frozenset(accepted)
pattern = re.compile(r"_+")
text = "".... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
Args:
tensor_type (`str` or [`~utils.TensorType`], *optional*):
The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
unset, no modification is done.
prepend_batch_axis (`int`, *optional*, defaults to `False`):
... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
as_tensor = tf.constant
is_tensor = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
import torch
as_tensor = torch... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
if not is_tensor(inputs):
inputs = as_tensor(inputs)
except: # noqa E722
raise ValueError(
"Unable to create tensor, you should probably activate truncation and/or padding "
"with 'padding=True' 'truncation=True' to have batched tensors with the same ... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
artists_tokens, genres_tokens, lyrics_tokens = self.tokenize(artist, genres, lyrics)
artists_id, genres_ids, full_tokens = self._convert_token_to_id(artists_tokens, genres_tokens, lyrics_tokens)
attention_masks = [-INFINITY] * len(full_tokens[-1])
input_ids = [
self.convert_to_tenso... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
filename_prefix (`Optional[str]`, *optional*):
A prefix to add to the names of the files saved by the tokenizer.
"""
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
artists_file = os.... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
lyrics_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]
)
with open(lyrics_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.lyrics_encoder, ensure_ascii=False))
return (artists_file, genr... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
Args:
artists_index (`int`):
Index of the artist in its corresponding dictionary.
genres_index (`Union[List[int], int]`):
Index of the genre in its corresponding dictionary.
lyric_index (`List[int]`):
List of character indices, which eac... | 10,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/tokenization_jukebox.py |
class JukeboxConv1D(nn.Module):
def __init__(self, input_width, output_width):
super().__init__()
self.input_width = input_width
self.output_width = output_width
weight = torch.empty(input_width, output_width)
bias = torch.zeros(output_width)
self.weight = nn.Paramete... | 10,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxResConv1DBlock(nn.Module):
def __init__(self, config, conv_width, depth=1, res_scale=1.0):
super().__init__()
hidden_dim = config.res_convolution_multiplier * conv_width
dilation = config.res_dilation_growth_rate**depth
padding = dilation
self.res_scale = res_sc... | 10,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxResnet1D(nn.Module):
def __init__(self, config, conv_width, n_depth, reverse_dilation=False):
super().__init__()
self.dilation_cycle = config.res_dilation_cycle
res_scale = 1.0 if not config.conv_res_scale else 1.0 / math.sqrt(n_depth)
blocks = []
for depth in r... | 10,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxEncoderConvBlock(nn.Module):
def __init__(self, config, embed_dim, hidden_dim, depth, down_t, stride_t):
super().__init__()
blocks = []
filter_t = stride_t * 2
pad_t = stride_t // 2
if down_t > 0:
for i in range(down_t):
blocks.append(... | 10,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxEncoder(nn.Module):
def __init__(self, config, width, depth, levels, downs_t, strides_t):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
iterator = zip(list(range(self.levels)), downs_t, strides_t)
for i, down_t, stride_t in iterator... | 10,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxDecoderConvBock(nn.Module):
def __init__(self, config, embed_dim, hidden_dim, depth, down_t, stride_t, reverse_dilation=True):
self.embed_dim = embed_dim
self.hidden_dim = hidden_dim
super().__init__()
blocks = []
if down_t > 0:
filter_t = stride_t * ... | 10,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, hidden_states):
hidden_states = self.proj_in(hidden_states)
for block in self.upsample_block:
hidden_states = block(hidden_states)
return hidden_states | 10,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxDecoder(nn.Module):
def __init__(self, config, hidden_dim, depth, levels, downs_t, strides_t):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
for level, down_t, stride_t in zip(list(range(self.levels)), downs_t, strides_t):
self.l... | 10,364 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxBottleneckBlock(nn.Module):
def __init__(self, config: JukeboxVQVAEConfig):
super().__init__()
self.nb_discrete_codes = config.nb_discrete_codes
self.codebook_width = config.embed_dim
self.mu = config.lmu
self.threshold = 1.0
self.init = False
sel... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def init_codebook(self, hidden_states):
nb_discrete_codes = self.nb_discrete_codes
self.init = True
codes = self._tile(hidden_states)
self.codebook = codes[torch.randperm(codes.shape[0])][:nb_discrete_codes]
self.codebook_sum = self.codebook
self.codebook_elem = torch.one... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
_codebook_sum = torch.matmul(latent_states_onehot, hidden_states)
_codebook_elem = latent_states_onehot.sum(dim=-1) # nb_discrete_codes
codes = self._tile(hidden_states)
_random_codebook = codes[torch.randperm(codes.shape[0])][:nb_discrete_codes]
# Update centres
... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
norm_code = self.codebook_sum.view(nb_discrete_codes, codebook_width) / self.codebook_elem.view(
nb_discrete_codes, 1
)
self.codebook = usage * (norm_code) + (1 - usage) * _random_codebook
_codebook_prob = _codebook_elem / torch.sum(_codebook_elem) # prob of each bin... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
if hidden_states.shape[-1] == self.codebook_width:
prenorm = torch.norm(hidden_states - torch.mean(hidden_states)) / np.sqrt(np.prod(hidden_states.shape))
elif hidden_states.shape[-1] == 2 * self.codebook_width:
x1, x2 = hidden_states[..., : self.codebook_width], hidden_states[..., self.... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def quantise(self, latent_states):
# Calculate latent code latent_states
codebook_weights = self.codebook.t()
distance = (
torch.sum(latent_states**2, dim=-1, keepdim=True)
- 2 * torch.matmul(latent_states, codebook_weights)
+ torch.sum(codebook_weights**2, di... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
# Postprocess.
music_tokens = music_tokens.view(samples, seq_len)
return music_tokens
def decode(self, music_tokens):
samples, seq_len = music_tokens.shape
# Dequantise
dequantised_states = self.dequantise(music_tokens)
# Postprocess
dequantised_states = (
... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
# Update embeddings
if update_codebook:
update_metrics = self.update_codebook(hidden_states, music_tokens)
else:
update_metrics = {}
# Loss
commit_loss = torch.norm(dequantised_states.detach() - hidden_states) ** 2 / np.prod(hidden_states.shape)
# Passth... | 10,365 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxBottleneck(nn.Module):
def __init__(self, config, levels):
super().__init__()
self.levels = levels
self.level_blocks = nn.ModuleList()
for level in range(self.levels):
self.level_blocks.append(JukeboxBottleneckBlock(config))
def encode(self, raw_audio):
... | 10,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, input_audio):
music_tokens, quantised_states, commit_losses, metrics = [], [], [], []
for level in range(self.levels):
level_block = self.level_blocks[-level - 1]
hidden_states = input_audio[level]
sampled_tokens, quantised_state, commit_loss, metric... | 10,366 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxVQVAE(PreTrainedModel):
config_class = JukeboxVQVAEConfig
base_model_prefix = "vqvae"
def _init_weights(self, module):
if isinstance(module, nn.Embedding): # embed_tokens
module.weight.data.normal_(mean=0.0, std=0.02 * self.config.init_scale)
elif isinstance(module... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def __init__(self, config: JukeboxVQVAEConfig):
super().__init__(config)
downs_t = config.res_downs_t
strides_t = config.res_strides_t
if not config.sample_length:
downsamples = [stride**down for stride, down in zip(strides_t, downs_t)]
top_raw_to_tokens = np.prod... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
self.downsamples = [stride**down for stride, down in zip(strides_t, downs_t)]
self.hop_lengths = np.cumprod(self.downsamples)
self.levels = levels = config.levels
self.music_tokens_shapes = [
(int(self.sample_length // self.hop_lengths[-level - 1])) for level in range(levels)
... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
self.bottleneck = JukeboxBottleneck(config, levels)
def _decode(self, music_tokens, start_level=0, end_level=None):
# Decode
if end_level is None:
end_level = self.levels
latent_states = self.bottleneck.decode(music_tokens, start_level=start_level, end_level=end_level)
#... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Args:
music_tokens (`torch.LongTensor`):
Tensor of music tokens which will be decoded to raw audio by using the codebook. Each music token
should be an index to a corresponding `code` vector in the codebook.
start_level (`int`, *optional*):
Level a... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
return torch.cat(dequantised_states, dim=0) | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def _encode(self, raw_audio, start_level=0, end_level=None):
# Encode
if end_level is None:
end_level = self.levels
input_audio = raw_audio.permute(0, 2, 1).float()
latent_states = []
for level in range(self.levels):
encoder = self.encoders[level]
... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Args:
input_audio (`torch.Tensor`):
Raw audio which will be encoded to its discrete representation using the codebook. The closest `code`
form the codebook will be computed for each sequence of samples.
start_level (`int`, *optional*, defaults to 0):
... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
music_tokens = [torch.cat(music_tokens_level, dim=0) for music_tokens_level in zip(*music_tokens_list)]
return music_tokens | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def sample(self, n_samples):
music_tokens = [
torch.randint(0, self.nb_discrete_codes, size=(n_samples, *music_tokens_shape), device="cpu")
for music_tokens_shape in self.music_tokens_shapes
]
return self.decode(music_tokens)
def forward(self, raw_audio: torch.FloatT... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
>>> model = JukeboxVQVAE.from_pretrained("openai/jukebox-1b-lyrics").eval()
>>> set_seed(0)
>>> zs = [torch.randint(100, (4, 1))]
>>> model.decode(zs).shape
torch.Size([4, 8, 1])
```
"""
# Encode/Decode
input_audio = raw_audio.permute(0, 2, 1).float()
... | 10,367 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxMLP(nn.Module):
def __init__(self, config):
# a single channel is always used in original code
super().__init__()
embed_dim = config.hidden_size
hidden_dim = int(config.mlp_multiplier * embed_dim)
self.c_fc = JukeboxConv1D(embed_dim, hidden_dim)
self.c_p... | 10,368 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxLayerNorm(FusedLayerNorm):
def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super().__init__(normalized_shape, eps=eps, elementwise_affine=elementwise_affine)
self.width = np.prod(normalized_shape)
self.max_numel = 65535 * self.width
def forward(self... | 10,369 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxAttention(nn.Module):
def __init__(self, config, n_ctx, attn_func="dense_attn"):
super().__init__()
self.embed_dim = config.hidden_size
self.n_heads = config.n_heads
self.dropout = config.attn_dropout
hidden_dim = int(config.attention_multiplier * self.embed_dim)... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
# Sequence of length seq_len is factored as [blocks, seq_len // blocks]
self.attn_func = attn_func
if attn_func == "cross_attention":
self.qkv = self.decode_qkv
elif attn_func == "prime_attn":
self.qkv = self.prime_qkv
else:
self.qkv = self.factored_qk... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
self.blocks = config.blocks
self.spread = config.spread
if self.blocks is not None:
self.block_ctx = self.n_ctx // self.blocks
self.sample_t = 0
self.cache = {}
self.encoder_len = config.nb_relevant_lyric_tokens # length of the encoder input ids
self.record_... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def _attn(self, query_states, key_states, value_states, sample):
scale = self.scale
if self.training:
attention_weight = torch.matmul(query_states * scale, key_states * scale)
else:
attention_weight = torch.matmul(query_states, key_states)
attention_weight.mul... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
attention_weight = attention_weight * mask + -1e9 * (1 - mask)
attention_prob = F.softmax(attention_weight, dim=-1).type(attn_weight_type)
if self.record_attn:
self.attention_prob = attention_prob
if self.attn_func == "prime_attn":
# only keep music queries and ly... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def merge_heads(self, hidden_states):
hidden_states = hidden_states.permute(0, 2, 1, 3).contiguous()
new_hidden_states_shape = (*hidden_states.size()[:-2], hidden_states.size(-2) * hidden_states.size(-1))
return hidden_states.view(*new_hidden_states_shape) # in Tensorflow implem: fct merge_stat... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def dense_attn(self, query, key, value, sample):
query = self.split_heads(query)
key = self.split_heads(key, is_key=True)
value = self.split_heads(value)
context_states = self._attn(query, key, value, sample)
context_states = self.merge_heads(context_states)
return contex... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def block_attn(self, query, key, value, sample):
block_ctx = self.block_ctx
batch_size, seq_len, embed_dim = value.shape # For sample, query_len= 1, key_len = value_len = sample_t
if sample:
return self.dense_attn(query, key, value, sample).view(batch_size, 1, embed_dim)
els... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def transpose_block_attn(self, query, key, value, sample):
block_ctx = self.block_ctx
batch_size, seq_len, embed_dim = value.shape # For sample, query_len= 1, key_len = value_len = sample_t
if sample:
block_len = (seq_len - 1) % block_ctx
key = key[:, block_len::block_ct... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
value = value.view(batch_size, seq_len // block_ctx, block_ctx, embed_dim)
value = value.transpose(1, 2).contiguous()
value = value.view(batch_size * block_ctx, seq_len // block_ctx, embed_dim)
block_attn = self.dense_attn(query, key, value, sample)
block_attn = block_at... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def prev_block_attn(self, query, key, value, sample):
block_ctx = self.block_ctx
batch_size, seq_len, embed_dim = value.shape # For sample, query_len= 1, key_len = value_len = sample_t
if sample:
block = (seq_len - 1) // block_ctx
prev_l = (block - 1) * block_ctx
... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
key = key.view(batch_size, seq_len // block_ctx, block_ctx, embed_dim)[:, :-1, :, :]
key = torch.nn.functional.pad(key, (0, 0, 0, 0, 1, 0))
key = key.view(batch_size * seq_len // block_ctx, block_ctx, embed_dim)
value = value.view(batch_size, seq_len // block_ctx, block_ctx, embed_d... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
value = value.view(batch_size, nb_key_blocks, block_ctx, embed_dim)[:, -nb_query_blocks:]
value = value.contiguous().view(batch_size * nb_query_blocks, block_ctx, embed_dim)
return self.dense_attn(query, key, value, sample).view(batch_size, seq_len, embed_dim)
def summary_attn(self, qu... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
value = value[:, block_ctx - 1 : blocks * block_ctx - 1 : block_ctx, :]
value = torch.nn.functional.pad(value, (0, 0, 1, 0))
return self.dense_attn(query, key, value, sample).view(batch_size, 1, embed_dim)
else:
key = key.view(batch_size, blocks, seq_len // blocks, embed_dim)... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
batch_size, seq_len, embed_dim = value.shape # For sample, query_len= 1, key_len = value_len = sample_t
if sample:
raise NotImplementedError
else:
key = key.view(batch_size, blocks, seq_len // blocks, embed_dim)[:, :-1, -spread:, :]
key = torch.nn.functional.pad(key,... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def factored_qkv(self, hidden_states, last_encoder_hidden_states=None, sample=False):
curr_ctx = hidden_states.shape[1]
if last_encoder_hidden_states is not None:
raise TypeError("last_encoder_hidden_states should be None")
query, key, value = hidden_states.chunk(3, dim=2)
i... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def prime_qkv(self, hidden_states, last_encoder_hidden_states=None, sample=False):
curr_ctx = hidden_states.shape[1]
if last_encoder_hidden_states is not None:
raise TypeError("last_encoder_hidden_states should be None")
query, key, value = hidden_states.chunk(3, dim=2)
if sa... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def decode_qkv(self, hidden_states, last_encoder_hidden_states=None, sample=False):
curr_ctx = hidden_states.shape[1]
query = hidden_states
if sample:
if self.sample_t == 0:
self.cache["key"], self.cache["value"] = self.c_enc_kv(
last_encoder_hidde... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, hidden_states, last_encoder_hidden_states=None, sample=False):
curr_ctx = hidden_states.shape[1]
hidden_states = self.c_attn(hidden_states)
query, key, value, sample = self.qkv(
hidden_states, last_encoder_hidden_states=last_encoder_hidden_states, sample=sample
... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def _offset(self, curr_ctx):
if self.attn_func == "dense_attn":
return 0
return (self.sample_t - curr_ctx) % self.block_ctx
def _pad_to_block_ctx(self, hidden_states, query=False):
seq_len = hidden_states.shape[1]
offset = self._offset(seq_len) if query else 0
n_... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def _suff_cache_len(self):
"""
Precondition:
key and value are appended with the current context and self.sample_t reflects the 1-indexed sample
location in the context.
"""
previous_block_length = (self.sample_t - 1) % self.block_ctx + 1 + self.block_ctx
... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def _append_cache(self, key, value):
if "key" not in self.cache:
self.cache["key"] = key
self.cache["value"] = value
else:
old_key, old_value = key, value
key = torch.cat([self.cache["key"], old_key], dim=1)
value = torch.cat([self.cache["value... | 10,370 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxBlock(nn.Module):
def __init__(self, config, n_ctx, attn_func="dense_attn"):
super().__init__()
self.width = config.hidden_size
self.attn = JukeboxAttention(config, n_ctx, attn_func=attn_func)
self.layer_norm_0 = JukeboxLayerNorm(config.hidden_size)
self.mlp = J... | 10,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
output_states = self.layer_norm_1(residuals + hidden_states)
output_states = self.mlp(output_states)
if self.res_scale == 1.0:
output = residuals + hidden_states + output_states
else:
output = residuals + self.res_scale * (hidden_states + output_states)
return out... | 10,371 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxLayerStack(nn.Module):
def __init__(self, config, n_ctx):
super().__init__()
self.n_ctx = n_ctx
self.width = config.hidden_size
self.num_layers = config.num_layers
self.blocks = config.blocks
self.attention_pattern = config.attention_pattern
if se... | 10,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Args:
record_attn (`Union[bool,set]`):
Either a set of layer indices indicating which layers to store, or a boolean value indicating Whether
to dump all.
"""
def _should_record_attn(layer_idx):
if isinstance(record_attn, bool):
ret... | 10,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, hidden_states, last_encoder_hidden_states=None, sample=False):
# Blocks
for i, attn_layer in enumerate(self._attn_mods):
if attn_layer.attn_func == "cross_attention": # attend to the lyrics
hidden_states = attn_layer(
hidden_states, last... | 10,372 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxPositionalEmbedding(nn.Module):
def __init__(self, embed_dim, width):
super().__init__()
self.pos_emb = nn.Parameter(torch.empty((embed_dim, width)))
def forward(self):
pos_emb = self.pos_emb
return pos_emb | 10,373 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxConditionalAutoregressive(nn.Module):
def __init__(
self,
config,
n_ctx=None,
embed_dim=None,
audio_conditioning=False,
metadata_conditioning=False,
is_encoder=False,
):
"""
Autoregressive model on either lyric tokens or music ... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Args:
config (`JukeboxPriorConfig`):
Model configuration class with all the parameters of the model. Initializing with a config file does
not load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`]... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Whether or not the prior supports conditionning on artitst, genres, lyrics and timing.
is_encoder (`bool`, *optional*, defaults to `False`):
Whether the model is an encoder only model.
""" | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
super().__init__()
self.width = config.hidden_size
self.num_layers = config.num_layers
self.n_ctx = n_ctx if n_ctx is not None else config.n_ctx
self.embed_dim = embed_dim if embed_dim is not None else config.music_vocab_size
self.embed_tokens = nn.Embedding(self.embed_dim, confi... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
if config.merged_decoder:
# Merged piped model uses this setup
self.add_cond_after_transformer = False
self.share_embed_tokens_fc_proj_out = False
else:
self.add_cond_after_transformer = True
self.share_embed_tokens_fc_proj_out = True
if not i... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(
self,
tokens,
audio_conditioning=None,
metadata_conditioning=None,
last_encoder_hidden_states=None,
get_preds=False,
get_acts=False,
get_sep_loss=False,
):
"""
Args:
tokens (`torch.tensor`):
Can ... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
target = tokens # Target
hidden_states = self.embed_tokens(tokens)
# Shift by 1, and fill in start token
hidden_states = torch.cat((hidden_states[:, -1:], hidden_states[:, :-1]), dim=1)
if self.metadata_conditioning:
hidden_states[:, 0] = metadata_conditioning.view(batch_siz... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
hidden_states = self.fc_proj_out(hidden_states) # Predictions
loss_fn = nn.CrossEntropyLoss()
if get_sep_loss:
lyric_hidden_states = hidden_states[:, : self.encoder_len].reshape(-1, self.embed_dim)
token_hidden_states = hidden_states[:, self.encoder_len :].reshape(-1, self.embed... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def get_emb(self, sample_t, n_samples, tokens, audio_conditioning, metadata_conditioning):
if sample_t == 0:
hidden_states = torch.empty(n_samples, 1, self.width, dtype=self.embed_tokens.weight.dtype).to(
self.embed_tokens.weight.device
)
if self.metadata_cond... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def sample(
self,
n_samples,
audio_conditioning=None,
metadata_conditioning=None,
last_encoder_hidden_states=None,
temp=1.0,
top_k=0,
top_p=0.0,
get_preds=False,
sample_tokens=None,
):
if sample_tokens is None:
sampl... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
iter = tqdm(range(0, sample_tokens), leave=False)
for sample_t in iter:
iter.set_description(f"Ancestral sampling {sample_tokens} music tokens", refresh=True)
hidden_states, cond = self.get_emb(
sample_t, n_samples, tokens, audio_conditioning, metadata_con... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
hidden_states = self.transformer(
hidden_states, last_encoder_hidden_states=last_encoder_hidden_states, sample=True
)
if self.add_cond_after_transformer:
hidden_states = hidden_states + cond
hidden_states = self.fc_proj_out(hidden_s... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
tokens = torch.cat(sampled_tokens, dim=1)
if get_preds:
preds = torch.cat(preds, dim=1)
if get_preds:
return tokens, preds
else:
return tokens
def split_chunks(self, length, chunk_size):
n_passes = (length + chunk_size - 1) // chunk_size
... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def primed_sample(
self,
n_samples,
lyric_and_music_tokens,
audio_conditioning=None,
metadata_conditioning=None,
last_encoder_hidden_states=None,
temp=1.0,
top_k=0,
top_p=0.0,
get_preds=False,
chunk_size=None,
sample_tokens=... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
with torch.no_grad():
if get_preds:
preds = []
# Fill up key/value cache for past context by runing forward pass.
# We do so in chunks instead of doing the whole past in one forward pass to reduce max memory usage.
if chunk_size is None:
c... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
for current_chunk_size in tqdm(chunk_sizes, desc="Preparing past key value", leave=False):
sampled_audio_prime, conds_prime = [], []
for sample_t in range(start, start + current_chunk_size):
x_prime, cond_prime = self.get_emb(
sample_t, n_sampl... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
if get_preds:
if self.add_cond_after_transformer:
x_prime = x_prime + cond_prime
del cond_prime
x_primes.append(x_prime)
else:
del x_prime
if get_preds:
x_prime = torch.ca... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
itererator = tqdm(
range(len(sampled_audio), sample_tokens),
desc=f"Sampling {len(range(len(sampled_audio), sample_tokens))} music tokens",
leave=False,
)
for sample_t in itererator:
hidden_states, cond = self.get_emb(
... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
hidden_states = self.transformer(
hidden_states, last_encoder_hidden_states=last_encoder_hidden_states, sample=True
)
if self.add_cond_after_transformer:
hidden_states = hidden_states + cond
hidden_states = self.fc_proj_out(hidden_s... | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
music_tokens = torch.cat(sampled_audio, dim=1)
if get_preds:
preds = torch.cat(preds, dim=1)
if get_preds:
return music_tokens, preds
else:
return music_tokens | 10,374 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxMusicTokenConditioner(nn.Module):
"""
The `JukeboxMusicTokenConditioner` takes music tokens as an input (coresponding to the codes of the VQVAE's
codebook) and upsamples it using a single layer of decoder convolution block (the same is used in the VQVAE).
"""
def __init__(self, config,... | 10,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, music_tokens, raw_audio_conditionning=None):
"""
Args:
music_tokens (`torch.LongTensor`):
Music tokens form the uper level in range(nb_discrete_codes)
raw_audio_conditionning (`torch.LongTensor`, *optional*):
Audio used when prime... | 10,375 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxRangeEmbedding(nn.Module):
"""
The `JukeboxRangeEmbedding` interpolate the given [pos_start, pos_end] to obtain an equivalent of time positional
embedding of length `n_ctx`.
Binning process : For each pos in position tensor, find its bin [start,end) mapped to [0,1,...,bins-1] [start,end)
... | 10,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
def forward(self, pos_start, pos_end=None):
# Check if [pos_start,pos_end] in [pos_min, pos_max)
if not len(pos_start.shape) == 2:
raise TypeError(f"Expected shape with 2 dims, got {pos_start.shape}")
if not (self.pos_min <= pos_start).all() and (pos_start < self.pos_max).all():
... | 10,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
pos_end = pos_end.float()
# Interpolate so that [pos_start, ..., pos_end] <-> position tensor of length n_ctx
n_time = self.n_time
if n_time != 1:
interpolation = (
torch.arange(0, n_time, dtype=torch.float, device=pos_start.device).view(1, n_time) / n_time
... | 10,376 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
class JukeboxLabelConditioner(nn.Module):
def __init__(self, config, include_time_signal):
super().__init__()
embed_dim = config.hidden_size
timing_dims = config.timing_dims
sampling_rate = config.sampling_rate
nb_genres, nb_artists = config.metadata_dims
music_token... | 10,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
self.max_nb_genres = config.max_nb_genres
self.bow_genre_emb = nn.Embedding(nb_genres, embed_dim)
self.artist_emb = nn.Embedding(nb_artists, embed_dim)
self.include_time_signal = include_time_signal
if self.include_time_signal:
total_length_range = (config.min_duration * samp... | 10,377 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/jukebox/modeling_jukebox.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.