text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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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,
sliding_window=getattr(self.c... | 9,698 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MistralRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 9,699 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralDecoderLayer(nn.Module):
def __init__(self, config: MistralConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MistralAttention(config=config, layer_idx=layer_idx)
self.mlp = MistralMLP(config)
self.input_layernorm =... | 9,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
... | 9,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,700 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralRotaryEmbedding(nn.Module):
def __init__(self, config: MistralConfig, 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"... | 9,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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 ... | 9,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,701 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralPreTrainedModel(PreTrainedModel):
config_class = MistralConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MistralDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | 9,702 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralModel(MistralPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
Args:
config: MistralConfig
"""
def __init__(self, config: MistralConfig):
super().__init__(config)
self.padding_idx ... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
@add_start_docstrings_to_model_forward(MISTRAL_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[Cache] = None,
inputs_embe... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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 | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states,... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
hidden_states = layer_outputs[0]
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,)
... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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()
... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
config: MistralCon... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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 ... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask | 9,703 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 9,704 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralForCausalLM(MistralPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = MistralModel(config)
self.vocab_size = config.vocab_size
self.lm... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
@add_start_docstrings_to_model_forward(MISTRAL_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: Option... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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
... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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."
```"""
... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,705 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralForTokenClassification(MistralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MistralModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.... | 9,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
@add_start_docstrings_to_model_forward(MISTRAL_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,
at... | 9,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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).
"""
... | 9,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,706 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralForSequenceClassification(MistralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MistralModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize wei... | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
@add_start_docstrings_to_model_forward(MISTRAL_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.... | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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 | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,707 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class MistralForQuestionAnswering(MistralPreTrainedModel):
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.model = MistralModel(config) # diff with Llama: transformer->model
# Initialize w... | 9,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
@add_start_docstrings_to_model_forward(MISTRAL_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[t... | 9,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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... | 9,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.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,
... | 9,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,708 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_mistral.py |
class Qwen2MoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2MoeModel`]. It is used to instantiate a
Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a s... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2MoeModel`]
hidden_size (`int`, *optional*, defaults to 2048):
... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
`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
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construc... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model s... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`,... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the line... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabeling this will also
allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
router_aux_loss_coef (`float`, *option... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
```python
>>> from transformers import Qwen2MoeModel, Qwen2MoeConfig
>>> # Initializing a Qwen2MoE style configuration
>>> configuration = Qwen2MoeConfig()
>>> # Initializing a model from the Qwen1.5-MoE-A2.7B" style configuration
>>> model = Qwen2MoeModel(configuration)
>>> # Accessing the m... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
def __init__(
self,
vocab_size=151936,
hidden_size=2048,
intermediate_size=5632,
num_hidden_layers=24,
num_attention_heads=16,
num_key_value_heads=16,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_nor... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if use_sliding_window else None
... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout ... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.shared_expert_intermediate_size = shared_expert_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.norm... | 9,709 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/configuration_qwen2_moe.py |
class Qwen2MoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Qwen2MoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 9,710 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeRotaryEmbedding(nn.Module):
def __init__(self, config: Qwen2MoeConfig, 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_typ... | 9,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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 ... | 9,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,711 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeMLP(nn.Module):
def __init__(self, config, intermediate_size=None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias... | 9,712 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: Qwen2MoeConfig, layer_idx: Optional[int] = None):
... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embe... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = nn.Linear(self.hidden_size, self.num_hea... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# Ignore copy
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: bool = False,
use_cache: bool = False,
... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_st... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.floa... | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value | 9,713 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
"""
Qwen2Moe flash attention module, following Qwen2Moe attention module. This module inherits from `Qwen2MoeAttention`
as the weights of the module stays untouched. The only required change would be on the forward pass
where it needs to correctly call t... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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-... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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: bool = False,
use_cache: bool = False,
cache_position... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
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:
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
key_states, value_states = past_... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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_states.dtype
if input_dty... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
sliding_window=sliding_window,
is_causal=self.is_causal,
... | 9,714 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
"""
Qwen2Moe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`Qwen2MoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# Adapted from Qwen2MoeAttention.forward
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: bool = False,
us... | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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... | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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, -1, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_l... | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
causal_mask = attention_mask
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
... | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
# The q_len > 1 is nec... | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
return attn_output, None, past_key_value | 9,715 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeSparseMoeBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
# gating
self.gate = nn.Linear(config.hidden_size, config... | 9,716 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
hidden_states = hidden_states.view(-1, hidden_dim)
# router_logits: (batch * sequence_length, n_experts)
router_logits = self.gate(hidden_states)
... | 9,716 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# One hot encode the selected experts to create an expert mask
# this will be used to easily index which expert is going to be sollicitated
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
# Loop over all available experts in the model a... | 9,716 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
shared_expert_output = self.shared_expert(hidden_states)
shared_expert_output = F.sigmoi... | 9,716 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
class Qwen2MoeDecoderLayer(nn.Module):
def __init__(self, config: Qwen2MoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = QWEN2MOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
if (layer_idx not in config.mlp_on... | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
output_router_logits: O... | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.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.
output_router_logits (`bool`, *optional*):
Whether or not to return the logits of all the ... | 9,717 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py |
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