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and examples
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@ -125,8 +125,6 @@ class MyNewModelConfig(PretrainedConfig):
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>>> # Accessing the model configuration
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> configuration = model.config
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```
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```
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new_param (`int`, *optional*, defaults to `False`):
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A fun new parameter
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"""
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"""
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model_type = "my_new_model"
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model_type = "my_new_model"
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@ -203,7 +203,7 @@ class DummyAttention(nn.Module):
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past_key_value: Optional[Cache] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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hidden_shape = (*input_shape, -1, self.head_dim)
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@ -299,6 +299,7 @@ class DummyPreTrainedModel(PreTrainedModel):
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supports_gradient_checkpointing = True
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supports_gradient_checkpointing = True
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_no_split_modules = ["DummyDecoderLayer"]
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_no_split_modules = ["DummyDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_3 = True
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_supports_flash_attn_2 = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_flex_attn = True
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@ -203,7 +203,7 @@ class Multimodal1TextAttention(nn.Module):
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past_key_value: Optional[Cache] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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hidden_shape = (*input_shape, -1, self.head_dim)
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@ -299,6 +299,7 @@ class Multimodal1TextPreTrainedModel(PreTrainedModel):
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supports_gradient_checkpointing = True
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supports_gradient_checkpointing = True
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_no_split_modules = ["Multimodal1TextDecoderLayer"]
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_no_split_modules = ["Multimodal1TextDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_3 = True
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_supports_flash_attn_2 = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_flex_attn = True
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@ -201,7 +201,7 @@ class MyNewModel2Attention(nn.Module):
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past_key_value: Optional[Cache] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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hidden_shape = (*input_shape, -1, self.head_dim)
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@ -297,6 +297,7 @@ class MyNewModel2PreTrainedModel(PreTrainedModel):
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supports_gradient_checkpointing = True
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supports_gradient_checkpointing = True
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_no_split_modules = ["MyNewModel2DecoderLayer"]
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_no_split_modules = ["MyNewModel2DecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_3 = True
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_supports_flash_attn_2 = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_flex_attn = True
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@ -118,6 +118,8 @@ class NewTaskModelPreTrainedModel(PreTrainedModel):
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)
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)
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class NewTaskModelModel(NewTaskModelPreTrainedModel):
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class NewTaskModelModel(NewTaskModelPreTrainedModel):
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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# we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
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accepts_loss_kwargs = False
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def __init__(self, config: NewTaskModelConfig):
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def __init__(self, config: NewTaskModelConfig):
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super().__init__(config)
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super().__init__(config)
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@ -313,9 +315,11 @@ class NewTaskModelModel(NewTaskModelPreTrainedModel):
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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)
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)
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special_image_mask = special_image_mask.all(-1)
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else:
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else:
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special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
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special_image_mask = input_ids == self.config.image_token_id
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special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
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special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
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image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
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@ -433,32 +437,6 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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num_logits_to_keep: int = 0,
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num_logits_to_keep: int = 0,
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) -> Union[tuple, NewTaskModelCausalLMOutputWithPast]:
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) -> Union[tuple, NewTaskModelCausalLMOutputWithPast]:
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r"""
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, NewTaskModelForNewTask
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>>> model = NewTaskModelForNewTask.from_pretrained("google/new_task_model2-3b-mix-224")
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>>> processor = AutoProcessor.from_pretrained("google/new_task_model2-3b-mix-224")
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>>> prompt = "Where is the cat standing?"
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>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(**inputs,)
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>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Where is the cat standing?\nsnow"
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```
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Returns:
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Returns:
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"""
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"""
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vlm_outputs = super().forward(
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vlm_outputs = super().forward(
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@ -200,7 +200,7 @@ class SuperAttention(nn.Module):
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past_key_value: Optional[Cache] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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hidden_shape = (*input_shape, -1, self.head_dim)
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@ -296,6 +296,7 @@ class SuperPreTrainedModel(PreTrainedModel):
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supports_gradient_checkpointing = True
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supports_gradient_checkpointing = True
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_no_split_modules = ["SuperDecoderLayer"]
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_no_split_modules = ["SuperDecoderLayer"]
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_skip_keys_device_placement = ["past_key_values"]
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_skip_keys_device_placement = ["past_key_values"]
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_supports_flash_attn_3 = True
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_supports_flash_attn_2 = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_flex_attn = True
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@ -124,7 +124,7 @@ class SwitchFunctionAttention(nn.Module):
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past_key_value: Optional[Cache] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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hidden_shape = (*input_shape, -1, self.head_dim)
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@ -2,11 +2,122 @@ from transformers.models.llama.configuration_llama import LlamaConfig
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# Example where we only want to only add a new config argument and new arg doc
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# Example where we only want to only add a new config argument and new arg doc
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# here there is no `ARG` so we are gonna take parent doc
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class MyNewModelConfig(LlamaConfig):
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class MyNewModelConfig(LlamaConfig):
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r"""
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r"""
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new_param (`int`, *optional*, defaults to `False`):
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This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel
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A fun new parameter
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the MyNewModel-7B.
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e.g. [meta-my_new_model/MyNewModel-2-7b-hf](https://huggingface.co/meta-my_new_model/MyNewModel-2-7b-hf)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the MyNewModel model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MyNewModelModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. MyNewModel 1 supports up to 2048 tokens,
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MyNewModel 2 up to 4096, CodeLlama up to 16384.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
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understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
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results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'my_new_model3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'my_new_model3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'my_new_model3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'my_new_model3'. Scaling factor applied to high frequency components of the RoPE
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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head_dim (`int`, *optional*):
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The attention head dimension. If None, it will default to hidden_size // num_attention_heads
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```python
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>>> from transformers import MyNewModelModel, MyNewModelConfig
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>>> # Initializing a MyNewModel my_new_model-7b style configuration
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>>> configuration = MyNewModelConfig()
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>>> # Initializing a model from the my_new_model-7b style configuration
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>>> model = MyNewModelModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
|
"""
|
||||||
|
|
||||||
def __init__(self, mlp_bias=True, new_param=0, **super_kwargs):
|
def __init__(self, mlp_bias=True, new_param=0, **super_kwargs):
|
||||||
|
Loading…
Reference in New Issue
Block a user