text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
[`TFGPT2DoubleHeadsModel`]. | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of class... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
[`TFGPT2DoubleHeadsModel`].
... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
The dropout ratio to be used after the projection and activation.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
dot-product/softmax to float() when training with mixed precision. | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
Example:
```python
>>> from transformers import GPT2Config, GPT2Model
>>> # Initializing a GPT2 configuration
>>> configuration = GPT2Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = GPT2Model(configuration)
>>> # Accessing the model configurat... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initi... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_... | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | 9,183 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
class GPT2OnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_pas... | 9,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
@property
def num_attention_heads(self) -> int:
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) ->... | 9,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
... | 9,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
@property
def default_onnx_opset(self) -> int:
return 13 | 9,184 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py |
class IBertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.embedding_bit = 8
self.embedding_act_bit = 16
self.act_b... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.position_embedding_type = getattr(config, "position_embedding_type", "... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = IntLayerNorm(
config.hidden_size,
eps=config.layer_norm_eps,
output_bit=self.ln_output_bit,
quant_mode=se... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if position_ids is None:
if input_ids is not None:
# Create the position ids from the input token ids. Any padded tokens remain padded.
... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
if inputs_embeds is None:
inputs_embeds, inputs_embeds_scaling_factor = self.word_embeddings(input_ids)
else:
inputs_embeds_scaling_factor = None
token_type_embeddings, token_type_embeddings_scaling_factor = self.token_type_embeddings(token_type_ids)
embeddings, embeddin... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
embeddings, embeddings_scaling_factor = self.LayerNorm(embeddings, embeddings_scaling_factor)
embeddings = self.dropout(embeddings)
embeddings, embeddings_scaling_factor = self.output_activation(embeddings, embeddings_scaling_factor)
return embeddings, embeddings_scaling_factor
def create_p... | 9,185 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numbe... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# Q, K, V Linear layers
self.query = QuantLinear(
config.hidden_size,
self.all_head_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.key = ... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# Requantization (32bit -> 8bit) for Q, K, V activations
self.query_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.key_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.value_activation = QuantAct(self.act_bit, quant_mode=self.quant_mode)
self.out... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# Requantization
query_layer, query_layer_scaling_factor = self.query_activation(
mixed_query_layer, mixed_query_layer_scaling_factor
)
key_layer, key_layer_scaling_factor = self.key_activation(mixed_key_layer, mixed_key_layer_scaling_factor)
value_layer, value_layer_scaling_... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
scale = math.sqrt(self.attention_head_size)
attention_scores = attention_scores / scale
if self.quant_mode:
attention_sc... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_pr... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# requantization: 32-bit -> 8-bit
context_layer, context_layer_scaling_factor = self.output_activation(
context_layer, context_layer_scaling_factor
)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
output_scaling_factor = (
(... | 9,186 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32 | 9,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
self.dense = QuantLinear(
config.hidden_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_input_... | 9,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor... | 9,187 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.self = IBertSelfAttention(config)
self.output = IBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) ... | 9,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads) | 9,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs, self_outputs_scaling_factor = self.self(
hidden_states,
hidden_states_scaling_factor,
... | 9,188 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.intermediate_siz... | 9,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(self, hidden_states, hidden_states_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states, hidden_states_scaling_factor = self.intermediate_act_fn(
hidden_states, hidden_states_scaling_factor
)
... | 9,189 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32 | 9,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
self.dense = QuantLinear(
config.intermediate_size,
config.hidden_size,
bias=True,
weight_bit=self.weight_bit,
bias_bit=self.bias_bit,
quant_mode=self.quant_mode,
per_channel=True,
)
self.ln_input_act = QuantAct(self.ln_... | 9,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(self, hidden_states, hidden_states_scaling_factor, input_tensor, input_tensor_scaling_factor):
hidden_states, hidden_states_scaling_factor = self.dense(hidden_states, hidden_states_scaling_factor)
hidden_states = self.dropout(hidden_states)
hidden_states, hidden_states_scaling_factor... | 9,190 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.seq_len_dim = 1
self.attention = IBertAttention(config)
self.intermediate = IBertIntermediate(config)
self.output = IBertOutp... | 9,191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def forward(
self,
hidden_states,
hidden_states_scaling_factor,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs, self_attention_outputs_scaling_factor = self.attention(
hidden_states,
hidden_state... | 9,191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def feed_forward_chunk(self, attention_output, attention_output_scaling_factor):
attention_output, attention_output_scaling_factor = self.pre_intermediate_act(
attention_output, attention_output_scaling_factor
)
intermediate_output, intermediate_output_scaling_factor = self.intermedi... | 9,191 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.quant_mode = config.quant_mode
self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states,
... | 9,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
hidden_states_scaling_factor,
attention_mask,
layer_head_mask,
output_attentions,
)
hidden_... | 9,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
... | 9,192 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply t... | 9,193 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IBertConfig
base_model_prefix = "ibert" | 9,194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (QuantLinear, nn.Linear)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.... | 9,194 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertModel(IBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
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 laye... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
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 else self.config.use_return_dict | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# Prepare head ma... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
embedding_output, embedding_output_scaling_factor = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
emb... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
... | 9,195 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertForMaskedLM(IBertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config, add_pooling_layer=False)
self.lm_head = IBertLMHead(config)
# Initialize w... | 9,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
mask="<mask>",
)
def forward(
self,
inpu... | 9,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (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]`
kwarg... | 9,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_s... | 9,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,196 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder... | 9,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
def _tie_weights(self) -> None:
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
... | 9,197 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertForSequenceClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.classifier = IBertClassificationHead(config)
# Initialize weigh... | 9,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Option... | 9,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_s... | 9,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.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... | 9,198 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertForMultipleChoice(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply f... | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
inp... | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.co... | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None... | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
outputs = self.ibert(
flat_input_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=output_attentions,
... | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,199 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertForTokenClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = ... | 9,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[... | 9,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_s... | 9,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,200 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Li... | 9,201 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class IBertForQuestionAnswering(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Init... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
@add_start_docstrings_to_model_forward(IBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Op... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
outputs = self.ibert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_s... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,202 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py |
class QuantEmbedding(nn.Module):
"""
Quantized version of `torch.nn.Embedding`. Adds quantization-specific arguments on top of `torch.nn.Embedding`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
momentum (`float`, *optional*, defaults ... | 9,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
weight_bit=8,
momentum=0.95,
quant_mode=False,
):
super()._... | 9,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.weight_bit = weight_bit
self.momentum = momentum
self.quant_mode = quant_mode
self.percentile_mode = False
self.weight_function = SymmetricQuantFunction.apply
def forward(self, x, positions=None, incremental_state=None):
if not self.quant_mode:
return (
... | 9,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.weight_scaling_factor = symmetric_linear_quantization_params(self.weight_bit, w_min, w_max, False)
self.weight_integer = self.weight_function(
self.weight, self.weight_bit, self.percentile_mode, self.weight_scaling_factor
)
emb_int = nn.functional.embedding(
x,
... | 9,203 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class QuantAct(nn.Module):
"""
Quantizes the given activation.
Args:
activation_bit (`int`):
Bitwidth for the quantized activation.
act_range_momentum (`float`, *optional*, defaults to `0.95`):
Momentum for updating the activation quantization range.
per_chan... | 9,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.activation_bit = activation_bit
self.act_range_momentum = act_range_momentum
self.quant_mode = quant_mode
self.per_channel = per_channel
self.percentile = False
self.act_function = SymmetricQuantFunction.apply
if not self.per_channel:
self.register_buffe... | 9,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
def forward(
self,
x,
pre_act_scaling_factor=None,
identity=None,
identity_scaling_factor=None,
specified_min=None,
specified_max=None,
):
x_act = x if identity is None else identity + x
# collect running stats if training
if self.train... | 9,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
# exponential moving average (EMA)
# use momentum to prevent the quantized values change greatly every iteration
elif self.act_range_momentum == -1:
self.x_min = torch.min(self.x_min, x_min)
self.x_max = torch.max(self.x_max, x_max)
else:
... | 9,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
if pre_act_scaling_factor is None:
# this is for the input quantization
quant_act_int = self.act_function(x, self.activation_bit, self.percentile, self.act_scaling_factor)
else:
quant_act_int = FixedPointMul.apply(
x,
pre_act_scaling_factor,
... | 9,204 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class QuantLinear(nn.Module):
"""
Quantized version of `torch.nn.Linear`. Adds quantization-specific arguments on top of `torch.nn.Linear`.
Args:
weight_bit (`int`, *optional*, defaults to `8`):
Bitwidth for the quantized weight.
bias_bit (`int`, *optional*, defaults to `32`):
... | 9,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
self.weight = nn.Parameter(torch.zeros([out_features, in_features]))
self.register_buffer("weight_integer", torch.zeros_like(self.weight))
self.register_buffer("fc_scaling_factor", torch.zeros(self.out_features))
if bias:
self.bias = nn.Parameter(torch.zeros(out_features))
... | 9,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
# assert that prev_act_scaling_factor is a scalar tensor
assert prev_act_scaling_factor is not None and prev_act_scaling_factor.shape == (1,), (
"Input activation to the QuantLinear layer should be globally (non-channel-wise) quantized. "
"Please add a QuantAct layer with `per_channel = ... | 9,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
bias_scaling_factor = self.fc_scaling_factor * prev_act_scaling_factor
if self.bias is not None:
self.bias_integer = self.weight_function(self.bias, self.bias_bit, False, bias_scaling_factor)
prev_act_scaling_factor = prev_act_scaling_factor.view(1, -1)
x_int = x / prev_act_scaling... | 9,205 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
class IntGELU(nn.Module):
"""
Quantized version of `torch.nn.GELU`. Adds quantization-specific arguments on top of `torch.nn.GELU`.
Args:
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"... | 9,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
def int_erf(self, x_int, scaling_factor):
b_int = torch.floor(self.coeff[1] / scaling_factor)
c_int = torch.floor(self.coeff[2] / scaling_factor**2)
sign = torch.sign(x_int)
abs_int = torch.min(torch.abs(x_int), -b_int)
y_int = sign * ((abs_int + b_int) ** 2 + c_int)
sca... | 9,206 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/quant_modules.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.