text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
kernel_init = jax.nn.initializers.normal(self.config.initializer_range)
if self.config.hidden_activation is None:
logger.warning_once(
"Gemma's activation function should be approximate GeLU and not exact GeLU. "
"Changing the activation function to `gelu_pytorch_tanh... | 3,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
self.gate_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
self.down_proj = nn.Dense(embed_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
self.up_proj = nn.Dense(inner_dim, use_bias=False, dtype=self.dtype, kernel_init=kernel_init)
def __call__(... | 3,315 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaDecoderLayer(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.input_layernorm = FlaxGemmaRMSNorm(self.config, dtype=self.dtype)
self.self_attn = FlaxGemmaAttention(self.config, dtype=self.dtype)
self.post_attention_layernorm = FlaxG... | 3,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
def __call__(
self,
hidden_states,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
... | 3,316 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GemmaConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(
... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_ids = jnp.zeros(input_shape, dtype="i4")
attention_mask = jnp.ones_like(input_ids)
position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids)... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
def init_cache(self, batch_size, max_length):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decodin... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
past_key_values: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train: bool = False,
... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
if position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
if attention_mask is Non... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
outputs = self.module.apply(
inputs,
jnp.array(input_ids, dtype="i4"),
jnp.array(attention_mask, dtype="i4"),
jnp.array(position_ids, dtype="i4"),
not train,
False,
output_attentions,
output_hidden_states,
return... | 3,317 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaLayerCollection(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGemmaDecoderLayer(self.config, dtype=self.dtype, name=str(i))
for i in range(self.config.num_hidden_layers)
]
def __call__(
... | 3,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
for block in self.blocks:
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = block(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
deterministic=deterministic,
... | 3,318 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaModule(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.hidden_size = self.config.hidden_size
embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
self.embed_tokens = nn.Embed(
self.config.vo... | 3,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
input_embeds = input_embeds * (self.config.hidden_size**0.5)
outputs = self.layers(
input_embeds,
position_ids=position_ids,
attention_mask=attention_mask,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attent... | 3,319 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaModel(FlaxGemmaPreTrainedModel):
module_class = FlaxGemmaModule | 3,320 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaForCausalLMModule(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxGemmaModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,... | 3,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
# Ignore copy
def __call__(
self,
input_ids,
attention_mask=None,
position_ids=None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
... | 3,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
if not return_dict:
return (lm_logits,) + outputs[1:]
return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) | 3,321 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class FlaxGemmaForCausalLM(FlaxGemmaPreTrainedModel):
module_class = FlaxGemmaForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape | 3,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
past_key_values = self.init_cache(batch_size, max_length)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since Gemma uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention... | 3,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
return model_kwargs | 3,322 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py |
class GemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | 3,323 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | 3,324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaRotaryEmbedding(nn.Module):
def __init__(self, config: GemmaConfig, 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", co... | 3,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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 ... | 3,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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... | 3,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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... | 3,325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GemmaConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hid... | 3,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 3,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 3,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 3,326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaDecoderLayer(nn.Module):
def __init__(self, config: GemmaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
self.mlp = GemmaMLP(config)
self.input_layernorm = Gemma... | 3,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cac... | 3,327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaPreTrainedModel(PreTrainedModel):
config_class = GemmaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GemmaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | 3,328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaModel(GemmaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]
Args:
config: GemmaConfig
"""
def __init__(self, config: GemmaConfig):
super().__init__(config)
self.padding_idx = config.p... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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 and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
# embed positions
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_leng... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(atte... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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.
... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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(... | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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
) | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
return causal_mask | 3,329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaForCausalLM(GemmaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = GemmaModel(config)
self.vocab_size = config.vocab_size
self.lm_head ... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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, whic... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"What is your favorite condiment?"
```"""
output_attentions = output_attentions if output_at... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaForSequenceClassification(GemmaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GemmaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights a... | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
@add_start_docstrings_to_model_forward(GEMMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.Fl... | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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... | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_va... | 3,332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class GemmaForTokenClassification(GemmaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GemmaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classi... | 3,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
@add_start_docstrings_to_model_forward(GEMMA_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,
atte... | 3,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.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).
"""
... | 3,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden... | 3,333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py |
class TimeSeriesTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TimeSeriesTransformerModel`]. It is used to
instantiate a Time Series Transformer model according to the specified arguments, defining the model architecture.
Instantiating a confi... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
Args:
prediction_length (`int`):
The prediction length for the decoder. In other words, the prediction horizon of the model. This value is
typically dictated by the dataset and we recommend to set it appropriately.
context_length (`int`, *optional*, defaults to `prediction_length... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
The size of the target variable which by default is 1 for univariate targets. Would be > 1 in case of
multivariate targets.
scaling (`string` or `bool`, *optional* defaults to `"mean"`):
Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`,... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
The number of static categorical features.
num_static_real_features (`int`, *optional*, defaults to 0):
The number of static real valued features.
cardinality (`list[int]`, *optional*):
The cardinality (number of different values) for each of the static categorical features. Shou... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
Number of encoder layers.
decoder_layers (`int`, *optional*, defaults to 2):
Number of decoder layers.
encoder_attention_heads (`int`, *optional*, defaults to 2):
Number of attention heads for each attention layer in the Transformer encoder.
decoder_attention_heads (`int`... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the encoder, and decoder.
encoder_layerdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for the attention and fully connected layers for each encoder layer.
... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
The standard deviation of the truncated normal weight initialization distribution.
use_cache (`bool`, *optional*, defaults to `True`):
Whether to use the past key/values attentions (if applicable to the model) to speed up decoding. | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
Example:
```python
>>> from transformers import TimeSeriesTransformerConfig, TimeSeriesTransformerModel
>>> # Initializing a Time Series Transformer configuration with 12 time steps for prediction
>>> configuration = TimeSeriesTransformerConfig(prediction_length=12)
>>> # Randomly initializing a ... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
def __init__(
self,
prediction_length: Optional[int] = None,
context_length: Optional[int] = None,
distribution_output: str = "student_t",
loss: str = "nll",
input_size: int = 1,
lags_sequence: List[int] = [1, 2, 3, 4, 5, 6, 7],
scaling: Optional[Union[str... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
decoder_layerdrop: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
num_parallel_samples: int = 100,
init_std: float = 0.02,
use_cache=True,
**kwargs,
):
# time series specific configuration
self.prediction_length = predict... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
"The cardinality should be a list of the same length as `num_static_categorical_features`"
)
self.cardinality = cardinality
else:
self.cardinality = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(embedding_dimension) != num_... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
# Transformer architecture configuration
self.feature_size = input_size * len(lags_sequence) + self._number_of_features
self.d_model = d_model
self.encoder_attention_heads = encoder_attention_heads
self.decoder_attention_heads = decoder_attention_heads
self.encoder_ffn_dim = enco... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
@property
def _number_of_features(self) -> int:
return (
sum(self.embedding_dimension)
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
... | 3,334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py |
class TimeSeriesFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
... | 3,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
return torch.cat(
[
embed(cat_feature_slice.squeeze(-1))
for embed, cat_feature_slice in zip(self.embedders, cat_feature_slices)
],
dim=-1,
) | 3,335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__in... | 3,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
... | 3,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator
scale = torch.sqrt(variance + self.minimum_scale)
return (data - loc) / scale, loc, scale | 3,336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.dim = config.scaling_dim if ha... | 3,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculation
... | 3,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# If `default_scale` is provided, we use it, otherwise we use the scale
# of the batch.
if self.default_scale is None:
batch_sum = ts_sum.sum(dim=0)
batch_observations = torch.clamp(num_observed.sum(0), min=1)
default_scale = torch.squeeze(batch_sum / batch_observatio... | 3,337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "... | 3,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
def forward(
self, data: torch.Tensor, observed_indicator: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Parameters:
data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`):
input for Batch norm calculatio... | 3,338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weig... | 3,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@staticmethod
def _init_weight(out: nn.Parameter) -> nn.Parameter:
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
position_enc = np.a... | 3,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
@torch.no_grad()
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor:
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
bsz, seq_len = input_ids_shape[:2]
positions = torch.arange(
past_key_values_length, past_key_values_length ... | 3,339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x) | 3,340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
class TimeSeriesTransformerAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = F... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 3,341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py |
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