text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class GPT2Attention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.config = config
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
self.scale_attn_weights = config.scale_attn_weights
self.is_cross_attention = is_cross_attention
# Layer-wise attention scaling, reordering, and upcasting
self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
self.layer_idx = layer_idx
self.reorder_and_up... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)])
# Prune conv1d layers
... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Preallocate attn_weights for `baddbmm`
attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device)
# Compute Scale Factor
scale_factor = 1.0
if self.scale_attn_weights:
scale_factor /= float(value.size(-1)) ** 0.5
if... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if not self.is_cross_attention:
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
mask_value = torch.finfo(attn_weights.dtype... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Downcast (if necessary) back to V's dtype (if in mixed-precision) -- No-Op if otherwise
if attn_weights.dtype != torch.float32:
raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32")
attn_weights = attn_weights.type(value.dtype)
attn_weights = se... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
query_states = self.q_attn(hidden_states)
key_states, value_states = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2)
attention_mask = encoder_attention_mask
else:
query_states, key_states, value_states = self.c_attn(hidden_states).split(self.split_size, dim=2... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
is_cross_attention = encoder_hidden_states is not None
is_causal = attention_mask is None and query_states.shape[-2] > 1 and not is_cross_attention | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
using_eager = self.config._attn_implementation == "eager"
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and (output_attentions or head_mask is not None):
using_eager = True... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# not necessarily to eager (if mentionned options are provided).
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if using_eager and self.reorder_and_upcast_attn:
attn_output, attn_weights = self._upcast_and_reordered_attn(
query_states, key_states, value_states, attention_mask, head_mask
)
else:
attn_output, attn_weights = attention_interface(
self,
... | 9,150 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2MLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
... | 9,151 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2Block(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self... | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
def forward(
self,
hidden_states: Optional[Tuple[torch.FloatTensor]],
layer_past: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,... | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
outputs = attn_outputs[1:]
# residual connection
hidden_states = attn_output + residual | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
"cross-attentio... | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
outputs = outputs + cross_attn_outputs[2:] # add cross attentions if we output attention weights | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
if use_cache:
outputs = (hidden_states,) + outputs
else:
... | 9,152 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
is_paralleli... | 9,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.norm... | 9,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
... | 9,153 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2DoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not. | 9,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided):
Multiple choice classification loss.
logits (`torch.... | 9,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states... | 9,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
GPT2Attentions weights after the attention softmax, used to compute the weighted average in the
self-attention heads.
"""
loss: Optional[torch.FloatTensor] = None
mc_loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
mc_logits: torch.FloatTensor = None
past_ke... | 9,154 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2Model(GPT2PreTrainedModel):
_supports_param_buffer_assignment = False
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embed... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(PARALLELIZE_DOCSTRING)
def parallelize(self, device_map=None):
# Check validity of device_map
warnings.warn(
"`GPT2Model.parallelize` is deprecated and will be removed in v5 of Transformers, you should load your"
" model with `device_map='balanced'` in t... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
self.wte = self.wte.to(self.first_device)
self.wpe = self.wpe.to(self.first_device)
# Load onto devices
for k, v in self.device_map.items():
for block in v:
cuda_device = "cuda:" + str(k)
self.h[block] = self.h[block].to(cuda_device)
# ln_f to ... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.model_parallel = False
self.device_map = None
s... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads) | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] =... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
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
... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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()
... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if past_key_values is None:
past_length = 0
past_key_values = tuple([None] * len(self.h))
else:
past_length = past_key_values[0][0].size(-2)
if position_ids is None:
position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, ... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Attention mask.
_use_sdpa = self._attn_implementation == "sdpa" and output_attentions is False and head_mask is None
attention_mask = attention_mask.view(batch_size, -1) if attention_mask is not None else None
if self._attn_implementation == "flash_attention_2":
attention_mask = at... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, None, None, :] | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.add_cross_attention and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_stat... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# head_mask has shape n_layer x batch x n_heads x N x N
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
if token_type_ids is not None:
... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
all_hidden_states = () if output_hidden_states else None
for i in range(len(self.h)):
bloc... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
head_mask = head_mask.to(hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,) | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
None,
attention_mask,
head_mask[i],
encoder_hidden_states,... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
hidden_states = outputs[0]
if use_cache is True:
presents = presents + (outputs[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
if self.config.add_cross_attention:
all_cr... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if not return_dict:
return tuple(
v
for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_... | 9,155 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2LMHeadModel(GPT2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Model parallel... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(PARALLELIZE_DOCSTRING)
def parallelize(self, device_map=None):
warnings.warn(
"`GPT2LMHeadModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should load"
" your model with `device_map='balanced'` in the call to `from_pretrained`. You... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.transformer.deparallelize()
self.transformer = self.tra... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
return_dict: Optional[bool] = None,
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
e... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].c... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
cross_attentions=transformer_out... | 9,156 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2DoubleHeadsModel(GPT2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 1
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bi... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(PARALLELIZE_DOCSTRING)
def parallelize(self, device_map=None):
warnings.warn(
"`GPT2DoubleHeadsModel.parallelize` is deprecated and will be removed in v5 of Transformers, you should"
" load your model with `device_map='balanced'` in the call to `from_pretrained`... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
self.multiple_choice_head = self.multiple_choice_head.to(self.transformer.first_device)
self.model_parallel = True | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings(DEPARALLELIZE_DOCSTRING)
def deparallelize(self):
warnings.warn(
"Like `parallelize`, `deparallelize` is deprecated and will be removed in v5 of Transformers.",
FutureWarning,
)
self.transformer.deparallelize()
self.transformer = self.tra... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=GPT2DoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
) -> Union[Tuple, GPT2DoubleHeadsModelOutput]:
r"""
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
Return:
Example:
```python
>>> import torch
>>> from transformers import AutoTokenizer, GPT2DoubleHeadsModel
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2")
>>> # ... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
>>> input_ids = torch.tensor(encoded_choices).unsqueeze(0) # Batch size: 1, number of choices: 2
>>> mc_token_ids = torch.tensor([cls_token_location]) # Batch size: 1
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_logits = outputs.logits
>>> mc_logits = outputs.mc_lo... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.transformer.first_device)
hidden_states = hidden_states.to(self.lm_head.weight.device)
lm_logits = self.lm_head(hidden_states)
mc_logits = self.multiple_choice_head(hidden_states, mc_to... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
if not return_dict:
output = (lm_logits, mc_logits) + transformer_outputs[1:]
if mc_loss is not None:
output = (mc_loss,) + output
return ((lm_loss,) + output) if lm_loss is not None else output
return GPT2DoubleHeadsModelOutput(
loss=lm_loss,
... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@staticmethod
def _reorder_cache(
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is call... | 9,157 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2ForSequenceClassification(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Model parallel
... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint="microsoft/DialogRPT-updown",
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] =... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
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_lab... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
u... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
assert (
self.config.pad_token_id is not None or batch_size == 1
), "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 fou... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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_... | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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,
) | 9,158 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2ForTokenClassification(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
classi... | 9,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
# fmt: off
@add_code_sample_docstrings(
checkpoint="brad1141/gpt2-finetuned-comp2",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_loss=0.25,
expected_output=[
"Lead",
... | 9,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTe... | 9,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
u... | 9,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,159 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class GPT2ForQuestionAnswering(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Model parallel
self.model_parallel ... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=QuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
real_checkpoint=_CHECKPOINT_FOR_DOC,
)
def ... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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
... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
outputs = self.transformer(
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_hi... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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).to(start_logits.device)
if len(end_positions.size(... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.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,
... | 9,160 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py |
class TFGPT2Tokenizer(keras.layers.Layer):
"""
This is an in-graph tokenizer for GPT2. It should be initialized similarly to other tokenizers, using the
`from_pretrained()` method. It can also be initialized with the `from_tokenizer()` method, which imports settings
from an existing standard tokenizer o... | 9,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_tf.py |
def __init__(self, vocab: Dict[str, int], merges: List[str], max_length: int = None, pad_token_id: int = None):
super().__init__()
self.pad_token_id = pad_token_id
self.max_length = max_length
self.vocab = vocab
self.merges = merges
self.tf_tokenizer = BytePairTokenizer(v... | 9,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_tf.py |
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, **kwargs):
"""Creates TFGPT2Tokenizer from pretrained GPT2Tokenizer
Args:
pretrained_model_name_or_path (Union[str, os.PathLike]): Path to pretrained model
Examples:
... | 9,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_tf.py |
def get_config(self):
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def call(self, x, max_length: int = None):
input_ids = self.tf_tokenizer(x)
attention_mask =... | 9,161 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_tf.py |
class GPT2TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT-2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be enco... | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extensi... | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
other word. (GPT2 tokenizer detect beginning of words by the preceding space).
""" | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = GPT2Tokenizer
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
... | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
assert self.add_prefix_space or not is_split_into_words, (
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it wi... | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
files = self._tokenizer.model.save(save_directory, name=filename_prefix)
return tuple(files) | 9,162 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py |
class FlaxConv1D(nn.Module):
features: int
use_bias: bool = True
dtype: Any = jnp.float32
precision: Any = None
@nn.compact
def __call__(self, inputs):
inputs = jnp.asarray(inputs, self.dtype)
kernel = self.param("kernel", jax.nn.initializers.normal(stddev=0.02), (self.features,... | 9,163 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
class FlaxGPT2Attention(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
def _split_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_states):
return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
"""
This function takes projected key, value states from a single input token and concatenates the states to cached
states from previous steps. This function is slighly adapted from the official Flax repository:
... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
if is_initialized:
*batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
# update key, value caches with our new 1d spatial slices
cur_index = cache_index.value
indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
key = lax.dynamic_update... | 9,164 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.