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# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerEncoderLayer(nn.Module):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TIME_SERIES_TRANSFORMER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
... | 3,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
"""
Args:
hidden_stat... | 3,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, traini... | 3,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self... | 3,342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerDecoderLayer(nn.Module):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TIME_SERIES_TRANSFORMER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = TIME_SERIES_TRANSFORMER_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
conf... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatT... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# Fully Connected
residual = hidden_states
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dro... | 3,343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerPreTrainedModel(PreTrainedModel):
config_class = TimeSeriesTransformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module... | 3,344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerEncoder(TimeSeriesTransformerPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TimeSeriesTransformerEncoderLayer`].
Args:
config: TimeSeriesTransformerConfig
"""
def __init__(self, config:... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
self.value_embedding = TimeSeriesValueEmbedding(feature_size=config.feature_size, d_model=config.d_model)
self.embed_positions = TimeSeriesSinusoidalPositionalEmbedding(
config.context_length + config.prediction_length, config.d_model
)
self.layers = nn.ModuleList([TimeSeriesTransfor... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optio... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**. | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices in... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
hidden_states = self.value_embedding(inputs_embeds)
embed_pos = self.embed_positions(inputs_embeds.size())
hidden_states = self.layernorm_embedding(hidden_states + embed_pos)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# expand attention... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != (len(self.layers)):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_st... | 3,345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerDecoder(TimeSeriesTransformerPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`TimeSeriesTransformerDecoderLayer`]
Args:
config: TimeSeriesTransformerConfig
"""
def __init__(self, config: TimeSeriesTrans... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
self.value_embedding = TimeSeriesValueEmbedding(feature_size=config.feature_size, d_model=config.d_model)
self.embed_positions = TimeSeriesSinusoidalPositionalEmbedding(
config.context_length + config.prediction_length, config.d_model
)
self.layers = nn.ModuleList([TimeSeriesTransfor... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] =... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not mas... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, ... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_a... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
input_shape = inputs_embeds.size()[:-1]
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
attention_mask = _prepare_4d_causal_attention_mask(
attention_mask, input_shape, inputs_embeds, past_key_values_length
... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
hidden_states = self.value_embedding(inputs_embeds)
embed_pos = self.embed_positions(inputs_embeds.size(), past_key_values_length=self.config.context_length)
hidden_states = self.layernorm_embedding(hidden_states + embed_pos)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, t... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# a... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPa... | 3,346 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerModel(TimeSeriesTransformerPreTrainedModel):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__(config)
if config.scaling == "mean" or config.scaling is True:
self.scaler = TimeSeriesMeanScaler(config)
elif config.scaling == "std... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@property
def _past_length(self) -> int:
return self.config.context_length + max(self.config.lags_sequence)
def get_lagged_subsequences(
self, sequence: torch.Tensor, subsequences_length: int, shift: int = 0
) -> torch.Tensor:
"""
Returns lagged subsequences of a given seque... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if max(indices) + subsequences_length > sequence_length:
raise ValueError(
f"lags cannot go further than history length, found lag {max(indices)} "
f"while history length is only {sequence_length}"
)
lagged_values = []
for lag_index in indices:
... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def create_network_inputs(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_features: Optional[torch.Tensor] = None,
past_observed_mask: Optional[torch.Tensor] = None,
future... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
context = past_values[:, -self.config.context_length :]
observed_context = past_observed_mask[:, -self.config.context_length :]
_, loc, scale = self.scaler(context, observed_context)
inputs = (
(torch.cat((past_values, future_values), dim=1) - loc) / scale
if future_valu... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if static_real_features is not None:
static_feat = torch.cat((static_real_features, static_feat), dim=1)
if static_categorical_features is not None:
embedded_cat = self.embedder(static_categorical_features)
static_feat = torch.cat((embedded_cat, static_feat), dim=1)
e... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if reshaped_lagged_sequence.shape[1] != time_feat.shape[1]:
raise ValueError(
f"input length {reshaped_lagged_sequence.shape[1]} and time feature lengths {time_feat.shape[1]} does not match"
)
# transformer inputs
transformer_inputs = torch.cat((reshaped_lagged_s... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqTSModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
past_observed_mask: torch.Te... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqTSModelOutput, Tuple]:
r"""
Returns: | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
Examples:
```python
>>> from huggingface_hub import hf_hub_download
>>> import torch
>>> from transformers import TimeSeriesTransformerModel
>>> file = hf_hub_download(
... repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_type="da... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
>>> # during training, one provides both past and future values
>>> # as well as possible additional features
>>> outputs = model(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_features"],
... past_observed_mask=batch["past_observed_mas... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
>>> last_hidden_state = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if encoder_outputs is None:
enc_input = transformer_inputs[:, : self.config.context_length, ...]
encoder_outputs = self.encoder(
inputs_embeds=enc_input,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
dec_input = transformer_inputs[:, self.config.context_length :, ...]
decoder_outputs = self.decoder(
inputs_embeds=dec_input,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
head_mask=decoder_head_mask,
cross_attn_head_... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
return Seq2SeqTSModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outp... | 3,347 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerForPrediction(TimeSeriesTransformerPreTrainedModel):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__(config)
self.model = TimeSeriesTransformerModel(config)
if config.distribution_output == "student_t":
self.distribution_output... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
if config.loss == "nll":
self.loss = nll
else:
raise ValueError(f"Unknown loss function {config.loss}")
# Initialize weights of distribution_output and apply final processing
self.post_init()
def output_params(self, dec_output):
return self.parameter_project... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@add_start_docstrings_to_model_forward(TIME_SERIES_TRANSFORMER_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=Seq2SeqTSModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
past_observed_mask: torch.Te... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Seq2SeqTSModelOutput, Tuple]:
r"""
Returns: | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
Examples:
```python
>>> from huggingface_hub import hf_hub_download
>>> import torch
>>> from transformers import TimeSeriesTransformerForPrediction
>>> file = hf_hub_download(
... repo_id="hf-internal-testing/tourism-monthly-batch", filename="train-batch.pt", repo_... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
>>> # during training, one provides both past and future values
>>> # as well as possible additional features
>>> outputs = model(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_features"],
... past_observed_mask=batch["past_observed_mas... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
>>> # during inference, one only provides past values
>>> # as well as possible additional features
>>> # the model autoregressively generates future values
>>> outputs = model.generate(
... past_values=batch["past_values"],
... past_time_features=batch["past_time_feature... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
outputs = self.model(
past_values=past_values,
past_time_features=past_time_features,
past_observed_mask=past_observed_mask,
static_categorical_features=static_categorical_features,
static_real_features=static_real_features,
future_values=future_va... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
prediction_loss = None
params = None
if future_values is not None:
params = self.output_params(outputs[0]) # outputs.last_hidden_state
# loc is 3rd last and scale is 2nd last output
distribution = self.output_distribution(params, loc=outputs[-3], scale=outputs[-2])
... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
return Seq2SeqTSPredictionOutput(
loss=prediction_loss,
params=params,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_atten... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@torch.no_grad()
def generate(
self,
past_values: torch.Tensor,
past_time_features: torch.Tensor,
future_time_features: torch.Tensor,
past_observed_mask: Optional[torch.Tensor] = None,
static_categorical_features: Optional[torch.Tensor] = None,
static_real_fea... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
Parameters:
past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`):
Past values of the time series, that serve as context in order to predict the future. The sequence size
of this tensor must be larger than th... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
The `past_values` is what the Transformer encoder gets as input (with optional additional features,
such as `static_categorical_features`, `static_real_features`, `past_time_features` and lags).
Optionally, missing values need to be replaced with zeros and indicated via the `past_observ... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
For multivariate time series, the `input_size` > 1 dimension is required and corresponds to the number
of variates in the time series per time step.
past_time_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_features)`):
Required time features, which ... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT,
where the position encodings are learned from scratch internally as parameters of the model, the Time
Series Transformer requires to provide additional time features. The Time Series Trans... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
future_time_features (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_features)`):
Required time features for the prediction window, which the model internally will add to ... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
These features serve as the "positional encodings" of the inputs. So contrary to a model like BERT,
where the position encodings are learned from scratch internally as parameters of the model, the Time
Series Transformer requires to provide additional time features. The Time Series Trans... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
The `num_features` here is equal to `config.`num_time_features` + `config.num_dynamic_real_features`.
past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, input_size)`, *optional*):
Boolean mask to indicate which `past_values` were... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
A typical example of a static categorical feature is a time series ID.
static_real_features (`torch.FloatTensor` of shape `(batch_size, number of static real features)`, *optional*):
Optional static real features which the model will add to the values of the time series.
Sta... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
Return:
[`SampleTSPredictionOutput`] where the outputs `sequences` tensor will have shape `(batch_size, number of
samples, prediction_length)` or `(batch_size, number of samples, prediction_length, input_size)` for
multivariate predictions.
"""
outputs = self(
... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
decoder = self.model.get_decoder()
enc_last_hidden = outputs.encoder_last_hidden_state
loc = outputs.loc
scale = outputs.scale
static_feat = outputs.static_features
num_parallel_samples = self.config.num_parallel_samples
repeated_loc = loc.repeat_interleave(repeats=num_p... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# greedy decoding
for k in range(self.config.prediction_length):
lagged_sequence = self.model.get_lagged_subsequences(
sequence=repeated_past_values,
subsequences_length=1 + k,
shift=1,
)
lags_shape = lagged_sequence.shape
... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
repeated_past_values = torch.cat(
(repeated_past_values, (next_sample - repeated_loc) / repeated_scale), dim=1
)
future_samples.append(next_sample)
concat_future_samples = torch.cat(future_samples, dim=1)
return SampleTSPredictionOutput(
sequences=co... | 3,348 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class MllamaConverter(TikTokenConverter):
def __init__(
self,
vocab_file,
special_tokens: List[str],
pattern: str,
model_max_length: int,
chat_template: Optional[str] = None,
**kwargs,
):
super().__init__(vocab_file, pattern=pattern)
self.a... | 3,349 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/convert_mllama_weights_to_hf.py |
class MllamaImageProcessor(BaseImageProcessor):
"""
Constructs a Mllama image processor. | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an effect if the input image is in the PIL format.
do_resize (`bool`, *optional*, defaults to `True`)... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
rescale_factor (`float`, *optional*, defaults to 0.0):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `se... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
model_input_names = ["pixel_values", "num_tiles", "aspect_ratio_ids", "aspect_ratio_mask"] | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
def __init__(
self,
do_convert_rgb: bool = True,
do_resize: bool = True,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: float = 1 / 255,
do_normalize: bool = True,... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.do_pad = do_pad
self.max_image_tiles = max_image_tiles | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
_validate_mllama_preprocess_arguments(self.do_resize, self.size, self.do_pad, self.max_image_tiles)
def preprocess(
self,
images: ImageInput,
do_convert_rgb: Optional[bool] = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: Opt... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
Args:
images (`ImageInput`):
A list of images to preprocess.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whet... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
The maximum number of tiles to split the image into.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"chan... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
Returns:
`BatchFeature` of the following structure:
- **pixel_values** (`TensorType`): The preprocessed pixel values.
- **aspect_ratio_ids** (`TensorType`): The aspect ratio ids of the images.
- **num_tiles** (`List[List[int]]`): The number of tiles for each i... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
image_std = image_std if image_std is not None else self.image_std
do_pad = do_pad if do_pad is not None else self.do_pad
max_image_tiles = max_image_tiles if max_image_tiles is not None else self.max_image_tiles | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
# iterate over images in a batch sample
for image in images:
# convert images to channels first format for faster processing
# LAST is slower for `pad` and not supported by `split_to_tiles`
data_format = ChannelDimension.FIRST
image = to_channe... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
# do_pad=False is not supported, validated
image = self.pad(
image=image,
size=size,
aspect_ratio=aspect_ratio,
input_data_format=data_format,
data_format=data_format,
)
i... | 3,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py |
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