text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
kwargs (`dict`, *optio... | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
... | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
if output_router_logits:
outputs += (router_logits,)
return outputs | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoePreTrainedModel(PreTrainedModel):
config_class = Qwen2MoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen2MoeDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_supports_sdpa = True
... | 9,718 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeModel(Qwen2MoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2MoeDecoderLayer`]
Args:
config: Qwen2MoeConfig
"""
def __init__(self, config: Qwen2MoeConfig):
super().__init__(config)
self.padding... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
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.config.use_cache | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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_ca... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is No... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,) | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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,
... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
position_embeddings=position_embeddings,
) | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits and layer_outputs[-1] is not None:
a... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
if v is not None
)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_k... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask with Phi3->Qwen2Moe
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: boo... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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 or using_sliding_window_cache)
and not output_attentions
):
if Atten... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
# SlidingWindowCache or StaticCache
if using_sliding_window_cache or using_static_cache:
target_length = past_key_values.get_max_cache_shape()
... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@staticmethod
# Copied from transformers.models.mistral.modeling_mistral.MistralModel._prepare_4d_causal_attention_mask_with_cache_position with Mistral->Qwen2Moe
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length:... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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.
target... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
The model's configuration class
past_key_values (`Cache`):
The cache class that is being used currently to generate
"""
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 ... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
sliding_attend_mask = torch.arange(target_length, device=device) <= (
cache_position.reshape(-1, 1) - config.sliding_window
)
diagonal_attend_mas... | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask | 9,719 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeForCausalLM(Qwen2MoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Qwen2MoeModel(config)
self.vocab_size = config.vocab_size
self... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Op... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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
... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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,
... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not ... | 9,720 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeForSequenceClassification(Qwen2MoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Qwen2MoeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@add_start_docstrings_to_model_forward(QWEN2MOE_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... | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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 | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,721 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeForTokenClassification(Qwen2MoePreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Qwen2MoeModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = conf... | 9,722 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@add_start_docstrings_to_model_forward(QWEN2MOE_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,
a... | 9,722 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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).
"""
... | 9,722 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,722 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeForQuestionAnswering(Qwen2MoePreTrainedModel):
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.model = Qwen2MoeModel(config) # diff with Llama: transformer->model
# Initializ... | 9,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
@add_start_docstrings_to_model_forward(QWEN2MOE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[... | 9,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
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 spa... | 9,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
... | 9,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,723 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class NemotronConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a ... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
Args:
vocab_size (`int`, *optional*, defaults to 256000):
Vocabulary size of the Nemotron model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`NemotronModel`]
hidden_size (`int`, *optional*, defaults to 6144):
... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
initializer_range (`float`, *optional*, defaults to 0.0134):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the normalization layers.
use_cache (`bool`, *optio... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
partial_rotary_factor (`float`, *optional*, defaults to 0.5): Percentage of the query and keys which will have rotary embedding.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attent... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
```python
>>> from transformers import NemotronModel, NemotronConfig
>>> # Initializing a Nemotron nemotron-15b style configuration
>>> configuration = NemotronConfig()
>>> # Initializing a model from the nemotron-15b style configuration
>>> model = NemotronModel(configuration)
>>> # Accessin... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
def __init__(
self,
vocab_size=256000,
hidden_size=6144,
intermediate_size=24576,
num_hidden_layers=32,
num_attention_heads=48,
head_dim=None,
num_key_value_heads=None,
hidden_act="relu2",
max_position_embeddings=4096,
initializer_r... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
self.head_dim = head_dim if head_dim is not None else hidden_size // num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.norm_eps = norm_eps
self.use_cache = use_cache
self.rope_th... | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 9,724 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/configuration_nemotron.py |
class NemotronLayerNorm1P(nn.LayerNorm):
def __init__(
self,
normalized_shape: Union[int, List[int], Size],
eps: float = 1e-5,
elementwise_affine: bool = True,
bias: bool = True,
device=None,
dtype=None,
):
super().__init__(normalized_shape, eps, e... | 9,725 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronRotaryEmbedding(nn.Module):
# Ignore copy
def __init__(
self,
config: NemotronConfig,
device=None,
):
super().__init__()
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = conf... | 9,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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 ... | 9,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,726 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | 9,727 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: NemotronConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | 9,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_val... | 9,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool ... | 9,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
if position_embeddings is not None:
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
... | 9,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
a... | 9,728 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronFlashAttention2(NemotronAttention):
"""
Nemotron flash attention module. This module inherits from `NemotronAttention` 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 ... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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-... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
# Ignore copy
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
ou... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shap... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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 the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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_s... | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value | 9,729 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronSdpaAttention(NemotronAttention):
"""
Nemotron attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`NemotronAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
# Ignore copy
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attent... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.v... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_ou... | 9,730 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronDecoderLayer(nn.Module):
# Ignore copy
def __init__(self, config: NemotronConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = NEMOTRON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
... | 9,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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,
... | 9,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are r... | 9,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states | 9,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value... | 9,731 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronPreTrainedModel(PreTrainedModel):
config_class = NemotronConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["NemotronDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | 9,732 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
class NemotronModel(NemotronPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NemotronDecoderLayer`]
Args:
config: NemotronConfig
"""
def __init__(self, config: NemotronConfig):
super().__init__(config)
self.padding... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
@add_start_docstrings_to_model_forward(NEMOTRON_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.FloatTens... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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 | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is No... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,) | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.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,
... | 9,733 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nemotron/modeling_nemotron.py |
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