text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
`[[[3, 3], [3, 3], [3, 3]], [[3, 3], [1, 3], [3, 3], [3, 1]], [[3, 3], [3, 3], [3, 1], [1, 3]], [[3, 3], [3, 1], [1, 3], [3, 3]]]`.
conv_layer_strides (`List[List[int]]`, *optional*):
A list of stage-wise strides. If `None`, defaults to:
`[[1, 2, 1], [2, 1, 1, 1], [2, 1, 1, 1], [2, 1, 1,... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to outpu... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
Examples:
```python
>>> from transformers import TextNetConfig, TextNetBackbone
>>> # Initializing a TextNetConfig
>>> configuration = TextNetConfig()
>>> # Initializing a model (with random weights)
>>> model = TextNetBackbone(configuration)
>>> # Accessing the model configuration
>... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
if conv_layer_kernel_sizes is None:
conv_layer_kernel_sizes = [
[[3, 3], [3, 3], [3, 3]],
[[3, 3], [1, 3], [3, 3], [3, 1]],
[[3, 3], [3, 3], [3, 1], [1, 3]],
[[3, 3], [3, 1], [1, 3], [3, 3]],
]
if conv_layer_strides is None:... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
self.depths = [len(layer) for layer in self.conv_layer_kernel_sizes]
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, 5)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.s... | 9,350 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/textnet/configuration_textnet.py |
class FalconLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
hidden_states = input @ self.weight.T
if self.bias is None:
return hidden_states
return hidden_states + self.bias | 9,351 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconRotaryEmbedding(nn.Module):
def __init__(self, config: FalconConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", ... | 9,352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 9,352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 9,352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 9,352 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconAttention(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
se... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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} and `num_heads`:"
f" {self.num_heads})."
) | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Layer-wise attention scaling
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
self.beta = self.inv_norm_factor
if config.new_decoder_architecture:
qkv_out_dim = (config.num_kv_heads * 2 + config.num_attention_heads) * self.head_dim
elif config.multi_query:
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# TODO (raushan): remove in v4.46 (RoPE is computed in the model, not in the decoder layers)
if config.rotary:
self.rotary_emb = FalconRotaryEmbedding(config=self.config)
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Spli... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
Returns:
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
value: [batch_size, seq_length, num_heads, head_dim]
"""
if self.new_decoder_architecture:
batch, seq_len, _ = fused_qkv.shape
qkv = fused_qkv.... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
query, key, value = [x.flatten(2, 3) for x in (query, key, value)]
return query, key, value
elif not self.multi_query:
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
Returns:
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
"""
# What we want to achieve is:
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
batch_size_and_num_heads, seq_length, _ = x.shape
batch_size = batch_... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Cache] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = Fa... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.h... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
kv_length = key_layer.shape[-2]
if self._use_sdpa and query_layer.device.type == "cuda" and attention_mask is not None:
# For torch<=2.1.2, SDPA with memory-efficient backend is bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issue... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if alibi is None:
if self._use_sdpa and not output_attentions:
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
attention_scores = query_layer @ key_layer.transpose(-1, -2)
attention_scores /= math.sqrt(self.head_dim) | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
attention_scores = F.softmax(attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
# It is unclear why neither dropout nor head_mask is applied here (while it is with alibi).
attn_output = attention_scores @ value_layer
attn_output = attn_output.view(batch_si... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
else:
if self._use_sdpa and not output_attentions and head_mask is None:
# We dispatch to SDPA's Flash Attention or Efficient kernels via this if statement instead of an
# inline conditional assignment to support both torch.compile's `dynamic=True` and `fullgraph=True`
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
attn_output = self.dense(attn_output)
else:
matmul_result = query_layer @ key_layer.transpose(-1, -2)
# change view to [batch_size, num_heads, q_length, kv_length]
attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
attention_logits *= self.inv_norm_factor
attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
# [batch_size, num_heads, q_length, kv_length]
... | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
attn_output = self.dense(attn_output)
if output_attentions:
return attn_output, layer_past, attention_probs
else:
return attn_output, layer_past | 9,353 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconFlashAttention2(FalconAttention):
"""
Falcon flash attention module. This module inherits from `FalconAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal wit... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Cache] = None,
head_mask: Optional[torch.Tensor] = None,
use_cache: bool = Fa... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.h... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
# to be able to avoid many of these transpose/reshape/view.
query_layer = query_layer.transpose(1, 2)
key_layer = key_l... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in float16 just to be sure everything works as expected.
input_dtype = query_layer.dtype
if input_dtyp... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_l... | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if not output_attentions:
attn_weights = None
return attn_output, layer_past, attn_weights | 9,354 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconMLP(nn.Module):
def __init__(self, config: FalconConfig):
super().__init__()
hidden_size = config.hidden_size
self.dense_h_to_4h = FalconLinear(hidden_size, config.ffn_hidden_size, bias=config.bias)
self.act = get_activation(config.activation)
self.dense_4h_to_h ... | 9,355 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconDecoderLayer(nn.Module):
def __init__(self, config: FalconConfig, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.self_attention = FALCON_ATTENTION_CLASSES[config._attn_implementation](config, layer_id... | 9,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if not config.parallel_attn:
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
else:
if config.num_ln_in_parallel_attn == 2:
# The layer norm befor... | 9,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def forward(
self,
hidden_states: torch.Tensor,
alibi: Optional[torch.Tensor],
attention_mask: torch.Tensor,
position_ids: Optional[torch.LongTensor] = None,
layer_past: Optional[Union[Cache, Tuple[torch.Tensor, torch.Tensor]]] = None,
head_mask: Optional[torch.Te... | 9,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Self attention.
attn_outputs = self.self_attention(
attention_layernorm_out,
layer_past=layer_past,
attention_mask=attention_mask,
position_ids=position_ids,
alibi=alibi,
head_mask=head_mask,
use_cache=use_cache,
o... | 9,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if (
self.config.new_decoder_architecture
and self.config.parallel_attn
and self.config.num_ln_in_parallel_attn == 1
):
mlp_layernorm_out = attention_layernorm_out
outputs = attn_outputs[1:]
# MLP.
mlp_output = self.mlp(mlp_layernorm_out)... | 9,356 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = FalconConfig
base_model_prefix = "transformer"
supports_gradient_checkpointing = True
_no_split_m... | 9,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def _init_weights(self, module: nn.Module):
"""Initialize the weights."""
if isinstance(module, nn.Linear) or isinstance(module, FalconLinear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/561... | 9,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Adapted from transformers.modeling_utils.PreTrainedModel._check_and_enable_sdpa
@classmethod
def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False) -> "PretrainedConfig":
_is_bettertransformer = getattr(cls, "use_bettertransformer", False)
if _is_bettertransformer:
... | 9,357 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconModel(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.use_alibi = config.alibi
# Embedding + LN Embedding
self.word_embeddings... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
@add_start_docstrings_to_model_forward(FALCON_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]... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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
)
use_cache = use_cache if use_cache is not None else self... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Compute alibi tensor: check build_alibi_tensor documentation
alibi = None
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
batch_size, seq_length, _ = inputs_embeds.shape
if self.use_alibi:
mask = (
torch.ones(
... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions, head_mask, alibi
)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape batch_size x num_heads x N x N... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
block.__call__,
hidden_states,
alibi,
causal_mask,
position_ids,
head_mask[i],
... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
position_embeddings=position_embeddings,
) | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
hidden_states = outputs[0]
if use_cache is True:
next_decoder_cache = outputs[1]
if output_attentions:
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
# Add last hidden state
hidden_states = self.ln_f(hidden_states)
... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
head_mask: torch.Tensor,
alibi: torch.Tensor,
):
# TODO: As of torch==2.2.0,... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# or... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if (
self.config._attn_implementation == "sdpa"
and not using_static_cache
and not output_attentions
and head_mask is None
and alibi is None... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
batch_size, sequence_length, _ = input_tensor.shape
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
atte... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# We take care to integrate alibi bias in the causal_mask here
if head_mask is None and alibi is not None:
alibi = alibi.reshape(batch_size, -1, *alibi.shape[1:])
causal_mask = torch.masked_fill(
alibi / math.sqrt(self.config.hidden_size // self.num_heads),
... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
return causal_mask
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_le... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
return causal_mask | 9,358 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconForCausalLM(FalconPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: FalconConfig):
super().__init__(config)
self.transformer = FalconModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
@add_start_docstrings_to_model_forward(FALCON_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,... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
) -> Union[Tuple[torch.Tensor], 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
`labels = input_ids` In... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pr... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_atte... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
batch_size, seq_length, vocab_size = shift_logits.shape
# Flatten the tokens
... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
def _reorder_cache(
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.be... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
# Get a copy of `beam_idx` on all the devices where we need those indices.
device_to_beam_idx = {
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
}
reordered_past = tuple(
(
layer_past[0].index_select(0... | 9,359 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconForSequenceClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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).
""" | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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_... | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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,
) | 9,360 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconForTokenClassification(FalconPreTrainedModel):
def __init__(self, config: FalconConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = FalconModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier... | 9,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pas... | 9,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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).
""" | 9,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
... | 9,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
if not return_dict:
output = (logits,) + transformer_outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=transformer_outputs.hidden_states,
attentions=... | 9,361 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconForQuestionAnswering(FalconPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = FalconModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply final processing
self.post_init() | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
@add_start_docstrings_to_model_forward(FALCON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = Non... | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`seque... | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
... | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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:
... | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.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,
... | 9,362 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/modeling_falcon.py |
class FalconConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
Args:
vocab_size (`int`, *optional*, defaults to 65024):
Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FalconModel`]
hidden_size (`int`, *optional*, defaults to 4544):
Dime... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all ... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
arguments are ignored, as the new decoder always uses parallel attention.
multi_query (`bool`, *optional*, defaults to `True`):
Whether to use multi-query attention in the decoder. ... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
Falcon models with RoPE support up to 2048 tokens.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rop... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddi... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
bos_token_id (`int`, *optional*, defaults to 11):
... | 9,363 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/falcon/configuration_falcon.py |
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