text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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outputs = self.mmbt(
input_modal=input_modal,
input_ids=input_ids,
modal_start_tokens=modal_start_tokens,
modal_end_tokens=modal_end_tokens,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
modal_token_type_ids=modal_token_... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
loss = None
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fc... | 10,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mmbt/modeling_mmbt.py |
class MegaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MegaModel`]. It is used to instantiate a Mega
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the Mega model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MegaModel`].
hidden_size (`int`, *optional*, defaults to 128):
Dimensio... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
or unidirectionally (`False`). Bidirectional EMA is incompatible with causal decoding, so this should be
False if you intend to use the model as a decoder.
shared_representation_size (`int`, *optional*, defaults to 64):
Dimensionality of the linear projection for shared representation of... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
Whether to normalize before (`True`) or after (`False`) passing through Mega encoder blocks
normalization_type (`str`, *optional*, defaults to `"scalenorm"`):
Type of normalization to use in Mega encoder blocks. Choose one of `"scalenorm"`, `"layernorm"`,
`"rmsnorm"`, `"batchnorm"`, or `... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for EMA self-attention
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_p... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
normalize_before_ffn (`bool`, *optional*, defaults to `True`):
Whether to normalize before (`True`) or after (`False`) the feed-forward portion of NFFN
nffn_activation_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the NFFN component.
max_positions (`i... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
The vocabulary size of the `token_type_ids` passed when calling [`MegaModel`]. Only used if
`add_token_type_embeddings = True`
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
relative_positional_bias (`str`, *optional*, defaults to `"rotary"`):
Type of relative positional encoding. Choose one of `"rotary"` or `"simple"`. If `"simple"` is selected,
`max_positions` is used as a limit on input size, while `"rotary"` extrapolates beyond `max_positions`.
is_decode... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
hidden states directly to LM head (`False`). Remains optional for compatibility with original
implementation | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
Examples:
```python
>>> from transformers import MegaConfig, MegaModel
>>> # Initializing a Mega configuration
>>> configuration = MegaConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = MegaModel(configuration)
>>> # Accessing the model configura... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
def __init__(
self,
vocab_size=30522,
hidden_size=128,
num_hidden_layers=4,
intermediate_size=256,
ema_projection_size=16,
bidirectional=True,
shared_representation_size=64,
use_chunking=False,
chunk_size=-1,
truncation=None,
... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
bos_token_id=0,
eos_token_id=2,
relative_positional_bias="rotary",
classifier_dropout=None,
use_cache=True,
add_lm_hidden_dense_layer=True,
**kwargs,
):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.activation = activation
self.attention_activation = attention_activation
self.intermediate_size = intermediate_size
self.ema_projection_size = ema_projection_size
... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
self.nffn_hidden_size = nffn_hidden_size
self.normalize_before_ffn = normalize_before_ffn
self.nffn_activation_dropout_prob = nffn_activation_dropout_prob
self.max_positions = max_positions
self.add_token_type_embeddings = add_token_type_embeddings
self.type_vocab_size = type_voc... | 10,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
class MegaOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 10,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/configuration_mega.py |
class MegaLM(nn.Module):
"The base class for our Mega encoder - given input IDs, embed text and return encoder output"
def __init__(self, mega_args, depth, vocab_size):
super().__init__()
self.mega_args = mega_args
self.embedding_layer = nn.Embedding(vocab_size, self.mega_args.encoder_e... | 10,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py |
Other options:
- batch_first: boolean indicating whether the batch dimension is first in input_ids (default: True, which
aligns with the HF tokenizer behavior)
- ignore_mask_value: the value in attention_mask that identifies tokens that should be ignored (default: 0,
which al... | 10,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py |
# pass through the Mega layers
# input is (time, batch, encoder dim) and output is the same
for encoder in self.encoders:
embeds = encoder(embeds, attention_mask)
# return according to the shape specified
if batch_first:
# (T, B, H) --> (B, T, H)
retu... | 10,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py |
class OriginalMegaForMaskedLM(nn.Module):
"A wrapper class for doing masked language modeling with Mega"
def __init__(self, mega_args, depth, vocab_size):
super().__init__()
self.mega = MegaLM(mega_args, depth, vocab_size)
self.mlm_head = nn.Linear(mega_args.encoder_embed_dim, vocab_siz... | 10,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py |
class MegaEmbeddings(nn.Module):
"""
Mega's basic implementation does not incorporate token type embeddings, so this is a stripped-down version of
RoBERTa's embeddings which optionally includes token types
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.word_embe... | 10,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None):
if (input_ids is None) and (inputs_embeds is None):
raise ValueError("Must provide one of input_ids or inputs_embeds")
elif input_ids is not None:
input_shape = input_ids.size()
device = input... | 10,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# the original Mega implementation did not include token type embeddings, so we add
# an option to use them if desired; if embeddings are present and token type IDs are
# not provided, we will use a registered buffer (which helps with tracing)
if self.use_token_types:
if token_type_i... | 10,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# access token type embeddings
token_type_embeddings = self.token_type_embeddings(token_type_ids)
# add the token type embeddings to the word embeddings
embeddings = inputs_embeds + token_type_embeddings
else:
embeddings = inputs_embeds
return embeddings | 10,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaSimpleRelativePositionalBias(nn.Module):
"""
Simple relative positional embeddings copied from the Mega repo; renamed variables for better readability
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.config = config
self.max_positions = self.config.m... | 10,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# seq_len * 2 - 1
bias = self.rel_pos_bias[(self.max_positions - seq_len) : (self.max_positions + seq_len - 1)]
# seq_len * 3 - 1
tile = F.pad(bias, (0, seq_len))
# (seq_len * 3 - 1) * seq_len
tile = torch.tile(tile, (seq_len,))
tile = tile[:-seq_len]
# seq_len x ... | 10,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaRotaryRelativePositionalBias(nn.Module):
"""
Rotary relative bias for positional information; similar in concept to RoPE (i.e. RoFormer) but taken from the Mega
repo due to differences in implementation.
When initialized, produces a positional bias which ranges from position 0 to config.max_p... | 10,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def __init__(self, config: MegaConfig):
super().__init__()
if config.hidden_size % 2 != 0:
raise RuntimeError("Rotary positional bias requires `hidden_size` to be a multiple of 2")
self.config = config
self.embed_dim = config.shared_representation_size
self.max_positi... | 10,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
@staticmethod
def get_sinusoid_embeddings(max_positions: int, embedding_dim: int):
half_dim = embedding_dim // 2
emb = math.log(10000) / half_dim
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
emb = torch.arange(max_positions, dtype=torch.float).unsqueeze(1... | 10,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(self, seq_len):
rotary_alpha = self.rotary(self.alpha.expand(seq_len, self.embed_dim))
rotary_beta = self.rotary(self.b_param.expand(seq_len, self.embed_dim))
bias = torch.einsum("mk,nk->mn", rotary_alpha, rotary_beta)
return bias | 10,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaDropout(nn.Module):
"""
A unified class for standard dropout functionality and featurewise dropout.
The original fairseq Mega repo used 2 classes for these, which included some unnecessary handling of training logic
and an unused `inplace` option. The original implementation used torch.nn.fun... | 10,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(self, input, batch_first: bool = False):
if self.is_featurewise:
if batch_first:
# (batch_size X sequence_length X feature_dimension)
# -> (batch_size X feature_dimension X sequence_length)
# -> (batch_size X sequence_length X feature_dimen... | 10,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
return F.dropout2d(input.permute(1, 2, 0), p=self.dropout_probability, training=self.training).permute(
2, 0, 1
)
else:
return F.dropout(input, p=self.dropout_probability, training=self.training) | 10,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaRMSNorm(nn.Module):
"""
RMSNorm used in Mega implementation. Differs from T5's RMSNorm by applying the weight prior to taking the square
root (as opposed to after in T5)
"""
def __init__(self, number_features, eps=1e-6, affine=True):
super().__init__()
self.num_features = ... | 10,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaScaleNorm(nn.Module):
"""
Scale normalization introduced in MEGA which is similar to RMSNorm, but uses a single parameter for scalar
multiplication instead of a vector, and applies over a specified dimension
"""
def __init__(self, dim, eps=1e-6, affine=True):
super().__init__()
... | 10,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaSequenceNorm(nn.Module):
"""
A wrapper class for various layer normalization options used in Mega. Used to handle differences in expectations on
input axis locations for different normalization methods.
"""
def __init__(self, norm_type, embedding_dim, eps=1e-5, affine=True, export=False):... | 10,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(self, input):
if isinstance(self.norm, nn.modules.batchnorm._BatchNorm):
if input.dim() != 3:
raise ValueError("BatchNorm inputs must be exactly 3-dimensional")
input = input.permute(1, 2, 0)
input = self.norm(input)
return input.permut... | 10,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaMultiDimensionDampedEma(nn.Module):
"""
Mega's Exponential Moving Average layer, largely left unmodified from the original repo with the exception of
variable names and moving away from the stateful representation of incremental decoding state. See
"https://arxiv.org/abs/2209.10655" for more d... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
kernel_dim = 2 * config.hidden_size if self.bidirectional else config.hidden_size
# renamed delta (damping_factor) and alpha (decay_factor) to be more descriptive of what the parameters are doing
self.damping_factor = nn.Parameter(torch.Tensor(kernel_dim, self.ndim, 1))
self.decay_factor = nn.Pa... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def _compute_ema_coefficients(self):
self._coeffs = None
# convert the alpha and delta parameters (kernel_dim x EMA projection size x 1) to [0, 1] with sigmoid
damping_factor = torch.sigmoid(self.damping_factor)
decay_factor = torch.sigmoid(self.decay_factor)
previous_timestep_we... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def _compute_efficient_ema_kernel(self, length: int):
# computes the kernel used for efficient damped EMA applied via FFT convolution
self._kernel = None
# p and q have shape (kernel_dim x ema_projection_size x 1)
damping_factor, previous_timestep_weight = self._compute_ema_coefficients(... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def get_ema_coefficients(self):
if self.training:
return self._compute_ema_coefficients()
else:
if self._coeffs is None:
self._coeffs = self._compute_ema_coefficients()
return self._coeffs
def get_ema_kernel(self, length: int):
kernel_size... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def fft_convolution(self, inputs, kernel, length):
# this is a wrapper for repeated use of EMA calculation via FFT (fast Fourier transform) convolution
inputs_fft = torch.fft.rfft(inputs.float(), n=2 * length)
kernel_fft = torch.fft.rfft(kernel.float(), n=2 * length)
convolved_sequence =... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (kernel_dim X ema_projection_size X 1)
damping_factor, previous_timestep_weight = self.get_ema_coefficients()
# (kernel_dim X ema_projection_size X 1+sequence_length)
vander = torch.arange(length + 1).to(damping_factor).view(1, 1, length + 1) * torch.log(
previous_timestep_weight
... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
past_vandermonde = vander[:, :, -1] * past_state
else:
past_ema_state = None
past_vandermonde = None | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (kernel_dim X ema_projection_size X sequence_length)
vander = vander[:, :, :-1]
kernel = (damping_factor * self.ema_expansion_matrix) * vander
kernel_proj = torch.einsum("dnl,dn->dl", kernel, self.kernel_projection_matrix * self.scale)
ema_output = self.fft_convolution(inputs, kernel_... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def one_ema_step(self, inputs, past_state=None):
damping_factor, previous_timestep_weight = self.get_ema_coefficients()
# (kernel_dim X ema_projection_size) x (batch_size X kernel_dim X 1)
# -> (batch_size X kernel_dim X ema_projection_size)
updated_state = (damping_factor * self.ema_exp... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(
self,
inputs,
attention_mask: Optional[torch.Tensor] = None,
prev_state: Optional[torch.Tensor] = None,
use_cache: bool = False,
) -> torch.Tensor:
"""
Mega's exponential moving average (EMA) sub-layer applied prior to single-headed (traditional) ... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Args:
inputs (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`):
Hidden state / embedding input to update via EMA based on FFT convolution
attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indicates which... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Returns:
`tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
inputs:
- **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden
states updated by EMA, with same shapes as i... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (sequence_length x batch_size x hidden_size) -> (batch_size x hidden_size x sequence_length)
inputs = inputs.permute(1, 2, 0)
# mask the input: output is a tensor with 0 in the masked positions
if attention_mask is not None:
inputs = inputs * (attention_mask.unsqueeze(1).type_as(in... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# if incremental decoding, return the new state along with the output
return out, updated_state
else:
# (hidden_size x sequence_length)
kernel = self.get_ema_kernel(seq_len)
fft_len = seq_len
s_index = 0
kernel_size = kernel.size(1)
... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
ema_output = self.fft_convolution(inputs, kernel, length=fft_len)[..., s_index : s_index + seq_len]
ema_output = ema_output.type_as(inputs)
# (batch_size X hidden_size X sequence_length) -> (sequence_length X batch_size X hidden_size)
gated_ema_output = F.silu(ema_output.permute(2, 0... | 10,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaGatedCrossAttention(nn.Module):
"""
Gated Structured State Attention for use in encoder-decoder model. See Mega paper for more details. Only
modifications from original implementation are variable names, removing the unnecessary `before_attn_fn` and
`static_kv` arguments, and the stateful repr... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
self.dropout = MegaDropout(self.config.dropout_prob, is_featurewise=self.config.use_feature_dropout)
self.hidden_dropout = MegaDropout(
self.config.hidden_dropout_prob, is_featurewise=self.config.use_feature_dropout
)
# Attention dropout is standard dropout
self.attention_dro... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
self.k_proj = nn.Linear(self.config.hidden_size, self.config.shared_representation_size)
self.v_proj = nn.Linear(self.config.hidden_size, self.config.hidden_size)
self.q_proj = nn.Linear(
self.config.hidden_size, 2 * self.config.hidden_size + self.config.shared_representation_size
)
... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def element_attention(self, query, key, key_padding_mask, pidx):
bsz, src_len, _ = key.size()
tgt_len = query.size(1) if pidx is None else pidx + 1
if key_padding_mask is not None:
# (batch_size X source_sequence_length) --> (batch_size X 1 X 1)
lengths = key_padding_mask... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
attn_weights = ACT2FN[self.attention_activation](qk).type_as(qk)
if key_padding_mask is not None:
attn_weights = attn_weights * key_padding_mask.unsqueeze(1)
return attn_weights
def softmax_attention(self, query, key, key_padding_mask, pidx):
bsz, src_len, _ = key.size()
... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# scaled attention
query = query * self.scaling
# (batch_size X target_sequence_length X source_sequence_length)
qk = torch.bmm(query, key.transpose(1, 2)) + bias
if key_padding_mask is not None:
qk = qk.masked_fill((1 - key_padding_mask).unsqueeze(1).to(torch.bool), float("... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Args:
query (`torch.Tensor` of shape `(target_sequence_length, batch_size, hidden_size)`):
The self (or target) sequence input used as query inputs for cross-attention
key (`torch.Tensor` of shape `(source_sequence_length, batch_size, hidden_size)`):
The cross (or... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
If provided, the hidden state returned from the previous timestep during incremental decoding; expects
that prior cross-attention keys and values will be the last two items in the tuple
output_attentions (`bool`, defaults to `False`):
Whether or not to return the cross-attent... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Returns:
`tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
inputs:
- **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) --
Hidden states from target sequence updated b... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
- **cross_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
source_sequence_length, config.hidden_size)` -- The cross-attention value state for use in the next step
of incremental decoding
""" | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
seq_len, bsz, embed_dim = query.size()
if embed_dim != self.config.hidden_size:
raise ValueError(
f"Unexpected embedding dimension received: input is {embed_dim} but expected {self.config.hidden_size}"
)
if past_key_values is not None:
# make sure the... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# use the self-attention cache to get the position id of the current step
prev_self_key = past_key_values[0]
num_incremental_steps = prev_self_key.size(1) + 1
else:
prev_cross_key = prev_cross_value = None
# we still need the position id if we're doing incremental... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (target_sequence_length X batch_size X 2*hidden_size + shared_representation_size)
query_projected = self.q_proj(full_query)
# split the query projections into separate components
# - residual_weight is passed through sigmoid and sent through elementwise multiplication to the gated/weighted ta... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if key is None:
if value is not None:
raise ValueError("Key and value must be `None` simultaneously")
projected_key = projected_value = None
else:
# (source_sequence_length X batch_size X shared_representation_size)
projected_key = self.k_proj(key)... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# if we're doing incremental decoding, k and v are None and need to be overwritten with past values
if past_key_values is not None:
projected_key = prev_cross_key
projected_value = prev_cross_value
# if we're returning the cache for later use, store these now for later return (c... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if key_padding_mask is not None:
if key_padding_mask.size(0) != bsz:
raise ValueError("Key padding mask does not align on the batch dimension")
if key_padding_mask.size(1) != ctx_len:
raise ValueError("Key padding mask does not align on the sequence length dimensi... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
projected_value = self.hidden_dropout(projected_value, batch_first=True)
kernel = self.attention_dropout(attn_weights)
# (batch_size X target_sequence_length X hidden_size)
# -> (target_sequence_length X batch_size X hidden_size)
weighted_targets = torch.bmm(kernel, projected_value).tran... | 10,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaMovingAverageGatedAttention(nn.Module):
"""
Pure PyTorch implementation of Mega block; see https://arxiv.org/abs/2209.10655 and original fairseq implementation
at https://github.com/facebookresearch/mega (copyright Meta Research, licensed under MIT License)
Differences from original implement... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def __init__(self, config: MegaConfig):
super().__init__()
self.config = config
self.activation = ACT2FN[self.config.activation]
self.scaling = (
self.config.shared_representation_size**-0.5 if self.config.attention_activation == "softmax" else None
)
self.dro... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
self.v_proj = nn.Linear(self.config.hidden_size, self.config.intermediate_size)
self.mx_proj = nn.Linear(
self.config.hidden_size,
self.config.shared_representation_size + self.config.intermediate_size + 2 * self.config.hidden_size,
)
self.h_proj = nn.Linear(self.config.i... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
self.softmax = nn.Softmax(dim=-1)
self.attention_function = (
self.softmax_attention if self.config.attention_activation == "softmax" else self.element_attention
)
def element_attention(self, query, key, padding_mask, causal_mask):
"""
Apply element-wise attention via re... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (sequence_length X sequence_length)
bias = self.rel_pos_bias(seq_len)
if seq_len != query.size(2):
if query.size(2) != 1:
raise ValueError("Size mismatch between Q and K in element attention")
# (1 X sequence_length)
bias = bias[-1:]
# (batc... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def softmax_attention(self, query, key, padding_mask, causal_mask):
"Standard softmax self-attention, as in the original Transformer paper"
seq_len = key.size(2)
# (sequence_length X sequence_length)
bias = self.rel_pos_bias(seq_len)
if seq_len != query.size(2):
if qu... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# apply causal mask (presumed to be 1/0 for not masked / masked)
# additive, but convert to 0/-inf (which is not explicitly in the Mega source code)
if causal_mask is not None:
additive_causal_mask = torch.zeros_like(causal_mask, dtype=qk.dtype)
additive_causal_mask = additive_ca... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
attn_weights = self.softmax(qk).type_as(qk)
return attn_weights
def forward(
self,
input,
padding_mask: Optional[torch.Tensor] = None,
causal_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[torch.Tensor]] = None,
output_attentions=False,
... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Args:
input (`torch.Tensor` of shape `(sequence_length, batch_size, hidden_size)`):
Hidden states to be updated by Mega's self-attention
padding_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indicates which inputs are to be ignore... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Whether to return self-attention weights
use_cache (`bool`, default `False`):
Whether to perfom incremental decoding; uses `past_key_values` as prior state, and returns the updated
states for use in the next step | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
Returns:
`tuple(torch.FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
inputs:
- **hidden_states** (`torch.FloatTensor` of shape `(sequence_length, batch_size, hidden_size)`) -- Hidden
states from target sequence updated by Mega'... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
- **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of
incremental decoding
- **self_ema_state** (*optional*, returned when `use_c... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
seq_len, bsz, embed_dim = input.size()
if embed_dim != self.config.hidden_size:
raise ValueError(f"Input embedding dimension should be {self.config.hidden_size}; received {embed_dim}")
# store inputs for residual connection and handle pre-norm if requested
residual = input
i... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# unpack the incremental state if provided
# assumed to be (self K, self V, self EMA state, cross K, cross V)
# also assumes that incremental decoding is working one token at a time, so input sequence length must be 1
if self.config.is_decoder and (past_key_values is not None):
if se... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# ema output is (sequence_length x batch_size x hidden_size)
# updated_ema_state will be None if use_cache=False; otherwise (batch_size, config.ndim)
ema_out, updated_ema_state = self.ema_gate(
input, attention_mask=padding_mask, prev_state=prev_ema_state, use_cache=use_cache
)
... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (sequence_length X batch_size X hidden_size)
# -> (sequence_length X batch_size X 2*hidden_size + config.shared_representation_size + config.intermediate_size)
# - residual_weight -> sigmoid -> applied to residual connection in torch.addcmul
# - query_key_gates -> split into two components: qu... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (sequence_length X batch_size X hidden_size)
residual_weight = torch.sigmoid(residual_weight)
# (sequence_length X batch_size X shared_representation_size + intermediate_size)
query_key_gates = F.silu(query_key_gates)
# split into two different tensors: one for Q/K usage and the othe... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# (sequence_length X batch_size X 2 X shared_representation_size)
# -> 2 tensors of (sequence_length X batch_size X shared_representation_size)
query, key = torch.unbind(query_key, dim=2)
# (sequence_length X batch_size X dimension)
# -> (batch_size X sequence_length X dimension)
... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if self.config.is_decoder:
# combine history and current to save updated state (if history is provided)
# when chunking is applied, the past states will be None at the end of the chunk, in
# which case, proceed as if no K/V history had been provided
# saved states are sto... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
# if not chunking, store as-is
if not self.config.use_chunking:
updated_self_key = key
updated_self_value = value
else:
curr_len = key.size(1) % self.config.chunk_size
if curr_len == 0:
# if we're chunking and ha... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
ctx_len = key.size(1) # potentially differs from seq_len because of incremental decoding
if not self.config.use_chunking:
# if we're not chunking, treat the entire sequence as one long chunk
# (batch_size X sequence_length X dimension) -> (batch_size X 1 X sequence_length X dimension)
... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
n_chunks = seq_len // self.config.chunk_size
query = query.reshape(bsz, n_chunks, self.config.chunk_size, self.config.shared_representation_size) | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
if ctx_len < self.config.chunk_size:
key = key.unsqueeze(1)
value = value.unsqueeze(1)
if padding_mask is not None:
padding_mask = padding_mask.unsqueeze(1)
else:
# (batch_size X sequence_length X dimension) -> (batch_size X... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
attn_weights = self.attention_function(query, key, padding_mask=padding_mask, causal_mask=causal_mask)
value = self.hidden_dropout(value, batch_first=True)
kernel = self.attention_dropout(attn_weights)
# (batch_size x n_chunks x chunk_size x intermediate_size) -> (sequence_length X batch_size ... | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
return_values = (out, attn_weights) if output_attentions else (out,)
if self.config.is_decoder:
return_values = return_values + (updated_self_key, updated_self_value, updated_ema_state)
return return_values | 10,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaNormalizedFeedForwardNetwork(nn.Module):
"""
Normalized feed-forward network used in Mega blocks. Left as-is from original Mega repo aside from retrieving args
from Hugging Face config
"""
def __init__(self, config: MegaConfig):
super().__init__()
self.config = config
... | 10,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
self.fc1 = nn.Linear(self.config.hidden_size, self.config.nffn_hidden_size)
self.fc2 = nn.Linear(self.config.nffn_hidden_size, self.config.hidden_size)
def forward(self, inputs):
residual = inputs
if self.prenorm:
inputs = self.norm(inputs)
hidden = self.activation(sel... | 10,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
class MegaBlock(nn.Module):
def __init__(self, config: MegaConfig):
super().__init__()
self.seq_len_dim = 1
self.mega_layer = MegaMovingAverageGatedAttention(config)
self.nffn = MegaNormalizedFeedForwardNetwork(config) if config.use_normalized_ffn else None
self.is_decoder = ... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
causal_mask: Optional[torch.LongTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
... | 10,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/mega/modeling_mega.py |
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