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class NatPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NatConfig
base_model_prefix = "nat"
main_input_name = "pixel_values"
def _init_weights(self, module... | 10,175 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatModel(NatPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
requires_backends(self, ["natten"])
self.config = config
self.num_levels = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_levels - ... | 10,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_he... | 10,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=NatModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
... | 10,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
embedding_output = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
embedding_output,
output_attentions=output_attentions,
output_hidden_states=output_hidden_state... | 10,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
return NatModelOutput(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
) | 10,176 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatForImageClassification(NatPreTrainedModel):
def __init__(self, config):
super().__init__(config)
requires_backends(self, ["natten"])
self.num_labels = config.num_labels
self.nat = NatModel(config)
# Classifier head
self.classifier = (
nn.Linear... | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=NatImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
... | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
outputs = self.nat(
pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels... | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
return NatImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
) | 10,177 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class NatBackbone(NatPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
requires_backends(self, ["natten"])
self.embeddings = NatEmbeddings(config)
self.encoder = NatEncoder(config)
self.num_features ... | 10,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
@add_start_docstrings_to_model_forward(NAT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
... | 10,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 512, 7, 7]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
... | 10,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
# TODO can we simplify this?
batch_size, num_channels, height, width = hidden_state.shape
hidden_state = hidden_state.permute(0, 2, 3, 1).... | 10,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
) | 10,178 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/nat/modeling_nat.py |
class OpenLlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OpenLlamaModel`]. It is used to instantiate an
Open-Llama model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Open-Llama model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`OpenLlamaModel`]
hidden_size (`int`, *optional*, defaults to 4096):
... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all ... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't upd... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
Example:
```python
>>> from transformers import OpenLlamaModel, OpenLlamaConfig
>>> # Initializing a Open-Llama open_llama-7b style configuration
>>> configuration = OpenLlamaConfig()
>>> # Initializing a model from the open_llama-7b style configuration
>>> model = OpenLlamaModel(configuratio... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
def __init__(
self,
vocab_size=100000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=Tr... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.use_memory_efficient_attention = kwargs.pop(
"use_memorry_efficient_attention", use_memory_efficient_attention
)
self.hidde... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configurati... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scal... | 10,179 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/configuration_open_llama.py |
class OpenLlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
OpenLlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states)... | 10,180 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaRotaryEmbedding(nn.Module):
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
super().__init__()
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
inv_freq = 1.0 / (self.base ** (torch.arange(0... | 10,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
self.register_buffer("sin_cached", e... | 10,181 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaLinearScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
"""OpenLlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.scaling_factor = scaling_... | 10,182 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaDynamicNTKScalingRotaryEmbedding(OpenLlamaRotaryEmbedding):
"""OpenLlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
self.sca... | 10,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
freqs = torch.outer(t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1)
self.regis... | 10,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
dropout_prob: float,
):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
self.down_proj = nn.Line... | 10,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: OpenLlamaConfig):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_hea... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
def _init_rope(self):
if self.config.rope_scaling is None:
self.rotary_emb = OpenLlamaRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = sel... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}") | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if self.config.use_memory_efficient_attention and xops is not None and self.training:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_output = xops.memory_... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = att... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.trans... | 10,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaDecoderLayer(nn.Module):
def __init__(self, config: OpenLlamaConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = OpenLlamaAttention(config=config)
self.mlp = OpenLlamaMLP(
hidden_size=self.hidden_size,
intermediate... | 10,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[boo... | 10,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
""" | 10,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
... | 10,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaPreTrainedModel(PreTrainedModel):
config_class = OpenLlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OpenLlamaDecoderLayer"]
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module,... | 10,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaModel(OpenLlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OpenLlamaDecoderLayer`]
Args:
config: OpenLlamaConfig
"""
def __init__(self, config: OpenLlamaConfig):
super().__init__(config)
self.pa... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
@add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
posi... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
if self.embed_layer_norm:
inputs_embeds = self.embed_layer_norm(inputs_embeds)
# embed positions
if self.config.use_memory_efficient_attention and self.training:
attention_mask = No... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
layer_ou... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from t... | 10,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaForCausalLM(OpenLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.model = OpenLlamaModel(config)
if config.shared_input_output_embedding:
self.lm_head = None
else:
self.lm_head = nn.Linear(config.hidden_size, confi... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
@add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Opt... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, OpenLlamaForCausalLM
>>> model = OpenLlamaForCausalLM.from_pretrained("openlm-research/open_llama_7b")
>>> tokenizer = AutoTokenizer.from_pretrained("openlm-research/open_llama_7b")
>>> prompt = "... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.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... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
hidden_states = outputs[0]
if self.config.shared_input_output_embedding:
logits = torch.einsum(
"blh,vh->blv", hidden_states.to(self.model.embed_tokens.weight.device), self.model.embed_tokens.weight
)
else:
logits = self.lm_head(hidden_states)
... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if p... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_p... | 10,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class OpenLlamaForSequenceClassification(OpenLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = OpenLlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
@add_start_docstrings_to_model_forward(OPEN_LLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token foun... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or label... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 10,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/open_llama/modeling_open_llama.py |
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super().__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
self.register_buffer("inv_freq", inv_freq)
def forward(self, pos_seq, bsz=None):
sinusoid_inp = torch.out... | 10,191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class PositionwiseFF(nn.Module):
def __init__(self, d_model, d_inner, dropout, pre_lnorm=False, layer_norm_epsilon=1e-5):
super().__init__()
self.d_model = d_model
self.d_inner = d_inner
self.dropout = dropout
self.CoreNet = nn.Sequential(
nn.Linear(d_model, d_i... | 10,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
# residual connection + layer normalization
output = self.layer_norm(inp + core_out)
return output | 10,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class RelPartialLearnableMultiHeadAttn(nn.Module):
def __init__(
self,
n_head,
d_model,
d_head,
dropout,
dropatt=0,
pre_lnorm=False,
r_r_bias=None,
r_w_bias=None,
layer_norm_epsilon=1e-5,
):
super().__init__()
self.... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if r_r_bias is None or r_w_bias is None: # Biases are not shared
self.r_r_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
self.r_w_bias = nn.Parameter(torch.FloatTensor(self.n_head, self.d_head))
else:
self.r_r_bias = r_r_bias
self.r_w_bias = r_w... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if mems is not None:
cat = torch.cat([mems, w], 0)
if self.pre_lnorm:
w_heads = self.qkv_net(self.layer_norm(cat))
else:
w_heads = self.qkv_net(cat)
r_head_k = self.r_net(r)
w_head_q, w_head_k, w_head_v = torch.chunk(w_heads, 3... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
r_head_k = r_head_k.view(rlen, self.n_head, self.d_head) # qlen x n_head x d_head
# compute attention score
rw_head_q = w_head_q + self.r_w_bias # qlen x bsz x n_head x d_head
AC = torch.einsum("ibnd,jbnd->ijbn", (rw_head_q, w_head_k)) # qlen x klen x bsz x n_head
rr_head_q = w_head... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
# compute attention probability
if attn_mask is not None and torch.sum(attn_mask).item():
attn_mask = attn_mask == 1 # Switch to bool
if attn_mask.dim() == 2:
attn_score = (
attn_score.float().masked_fill(attn_mask[None, :, :, None], mask_value).type_... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
# [qlen x bsz x n_head x d_head]
attn_vec = attn_vec.contiguous().view(attn_vec.size(0), attn_vec.size(1), self.n_head * self.d_head)
# linear projection
attn_out = self.o_net(attn_vec)
attn_out = self.drop(attn_out)
if self.pre_lnorm:
# residual connection
... | 10,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class RelPartialLearnableDecoderLayer(nn.Module):
def __init__(self, n_head, d_model, d_head, d_inner, dropout, layer_norm_epsilon=1e-5, **kwargs):
super().__init__()
self.dec_attn = RelPartialLearnableMultiHeadAttn(
n_head, d_model, d_head, dropout, layer_norm_epsilon=layer_norm_epsilo... | 10,194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class AdaptiveEmbedding(nn.Module):
def __init__(self, n_token, d_embed, d_proj, cutoffs, div_val=1, sample_softmax=False):
super().__init__()
self.n_token = n_token
self.d_embed = d_embed
self.cutoffs = cutoffs + [n_token]
self.div_val = div_val
self.d_proj = d_pro... | 10,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
self.emb_layers = nn.ModuleList()
self.emb_projs = nn.ParameterList()
if div_val == 1:
self.emb_layers.append(nn.Embedding(n_token, d_embed, sparse=sample_softmax > 0))
if d_proj != d_embed:
self.emb_projs.append(nn.Parameter(torch.FloatTensor(d_proj, d_embed)))
... | 10,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def forward(self, inp):
if self.div_val == 1:
embed = self.emb_layers[0](inp)
if self.d_proj != self.d_embed:
embed = nn.functional.linear(embed, self.emb_projs[0])
else:
param = next(self.parameters())
inp_flat = inp.view(-1)
e... | 10,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
embed_shape = inp.size() + (self.d_proj,)
embed = emb_flat.view(embed_shape)
embed.mul_(self.emb_scale)
return embed | 10,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = TransfoXLConfig
load_tf_weights = load_tf_weights_in_transfo_xl
base_model_prefix = "transformer"
... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def _init_weights(self, m):
"""Initialize the weights."""
classname = m.__class__.__name__
if classname.find("Linear") != -1:
if hasattr(m, "weight") and m.weight is not None:
self._init_weight(m.weight)
if hasattr(m, "bias") and m.bias is not None:
... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if hasattr(m, "cluster_bias") and m.cluster_bias is not None:
self._init_bias(m.cluster_bias)
if hasattr(m, "out_projs"):
for i in range(len(m.out_projs)):
if m.out_projs[i] is not None:
nn.init.normal_(m.out_projs[i], 0.0, self.con... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, layer: Optional[int] = -1):
"""
Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size. Take care of tying
weights embeddings afterwards if the model class has a *tie_weights()* method. | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Arguments:
new_num_tokens: (*optional*) int:
New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at
the end. Reducing the size will remove vectors from the end. If not provided or None: does nothing and
just ret... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
if new_num_tokens is None:
return self.get_input_embeddings()
new_num_tokens_layer, layer = self._get_new_num_tokens_layer(new_num_tokens, layer)
assert new_num_tokens_layer > 0, "The size of the new embedding layer cannot be 0 or less"
model_embeds = base_model._resize_token_embedd... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def _get_new_num_tokens_layer(self, new_num_tokens, layer):
embeddings = self.get_input_embeddings()
if layer == -1:
layer = len(embeddings.emb_layers) - 1
assert 0 <= layer <= len(embeddings.emb_layers) - 1
new_num_tokens_layer = (
new_num_tokens
- s... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
def _resize_token_embeddings(self, new_num_tokens, layer=-1):
embeddings = self.get_input_embeddings()
if new_num_tokens is None:
return embeddings
new_embeddings_layer = self._get_resized_embeddings(embeddings.emb_layers[layer], new_num_tokens)
embeddings.emb_layers[layer] =... | 10,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLModelOutput(ModelOutput):
"""
Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). | 10,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
mems (`List[torch.FloatTensor]` of length `config.n_layers`):
Contains pre-computed hidden-states (key and ... | 10,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 10,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
class TransfoXLSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models. | 10,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labe... | 10,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
shape `(batch_size, sequence_length, hidden_size)`. | 10,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/transfo_xl/modeling_transfo_xl.py |
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