easydel.modules.qwen2_vl.__init__

class easydel.modules.qwen2_vl.__init__.Qwen2VLConfig(vocab_size=152064, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_act='silu', max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=80, attention_dropout=0.0, vision_config=None, rope_scaling=None, vision_start_token_id=151652, vision_end_token_id=151653, vision_token_id=151654, image_token_id=151655, video_token_id=151656, **kwargs)

Bases: EasyDeLBaseConfig

This is the configuration class to store the configuration of a [Qwen2VLModel]. It is used to instantiate a Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).

Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information.

Parameters

• vocab_size (int, optional, defaults to 152064) – Vocabulary size of the Qwen2VL model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [Qwen2VLModel]

• hidden_size (int, optional, defaults to 8192) – Dimension of the hidden representations.

• intermediate_size (int, optional, defaults to 29568) – Dimension of the MLP representations.

• num_hidden_layers (int, optional, defaults to 80) – Number of hidden layers in the Transformer encoder.

• num_attention_heads (int, optional, defaults to 64) – Number of attention heads for each attention layer in the Transformer encoder.

• num_key_value_heads (int, optional, defaults to 8) – 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 converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to 32.

• hidden_act (str or function, optional, defaults to “silu”) – The non-linear activation function (function or string) in the decoder.

• max_position_embeddings (int, optional, defaults to 32768) – The maximum sequence length that this model might ever be used with.

• initializer_range (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

• rms_norm_eps (float, optional, defaults to 1e-05) – The epsilon used by the rms normalization layers.

• use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

• tie_word_embeddings (bool, optional, defaults to False) – Whether the model’s input and output word embeddings should be tied.

• rope_theta (float, optional, defaults to 1000000.0) – The base period of the RoPE embeddings.

• use_sliding_window (bool, optional, defaults to False) – Whether to use sliding window attention.

• sliding_window (int, optional, defaults to 4096) – Sliding window attention (SWA) window size. If not specified, will default to 4096.

• max_window_layers (int, optional, defaults to 80) – 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.

• vision_config (tp.Dict, optional) – The config for the visual encoder initialization.

• rope_scaling (tp.Dict, optional) –

  Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer max_position_embeddings, we recommend you to update this value 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, optional):

  Used with all rope types except ‘default’. The scaling factor to apply to the RoPE embeddings. In most scaling types, a factor of x will enable the model to handle sequences of length x * original maximum pre-trained length.

  original_max_position_embeddings (int, optional):

  Used with ‘dynamic’, ‘longrope’ and ‘llama3’. The original max position embeddings used during pretraining.

  attention_factor (float, optional):

  Used with ‘yarn’ and ‘longrope’. The scaling factor to be applied on the attention 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 linear ramp function. If unspecified, it defaults to 32.

  beta_slow (float, optional):

  Only used with ‘yarn’. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1.

  short_factor (tp.List[float], optional):

  Only used with ‘longrope’. The scaling factor to be applied to short 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 heads divided by 2

  long_factor (tp.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 heads divided by 2

  low_freq_factor (float, optional):

  Only used with ‘llama3’. Scaling factor applied to low frequency components of the RoPE

  high_freq_factor (float, optional):

  Only used with ‘llama3’. Scaling factor applied to high frequency components of the RoPE

```python >>> from transformers import Qwen2VLForConditionalGeneration, Qwen2VLConfig

>>> # Initializing a Qwen2VL style configuration
>>> configuration = Qwen2VLConfig()

>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2VLForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

get_partition_rules(*args, **kwargs)

Get the partition rules for the model parameters for use with distributed training.

These rules define how model parameters should be partitioned across multiple devices when using techniques like Fully Sharded Data Parallelism (FSDP), Sharded Parallelism (SP), and Tensor Parallelism (TP).

Returns

A tuple of tuples where each inner tuple contains:

• A regex pattern matching parameter names

• A PartitionSpec object specifying how to partition matching parameters

Return type

tuple

keys_to_ignore_at_inference = ['past_key_values']

model_type: str = 'qwen2_vl'

sub_configs: dict[str, 'PretrainedConfig'] = {'vision_config': <class 'easydel.modules.qwen2_vl.qwen2_vl_configuration.Qwen2VLVisionConfig'>}

class easydel.modules.qwen2_vl.__init__.Qwen2VLForConditionalGeneration(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

get_decoder()

get_input_embeddings()

get_output_embeddings()

get_static_arguments()

Returns a tuple of static arguments required by the module’s __call__ method.

Static arguments are those that don’t change across calls and can be potentially cached or handled differently by JIT compilation. This base implementation returns an empty tuple. Subclasses should override this if they have static arguments.

Returns

A tuple containing static arguments.

Return type

tp.Tuple

loss_type = 'ForCausalLM'

prepare_inputs_for_call(image_grid_thw: Optional[Union[Array, ndarray, bool, number]] = None, video_grid_thw: Optional[Union[Array, ndarray, bool, number]] = None, image_max_grid_size: int = None, video_max_grid_size: int = None, drop_ids: bool = True, **others)

Prepares keyword arguments before passing them to the module’s __call__ method.

This base implementation simply returns the kwargs as is. Subclasses can override this to modify or add arguments as needed (e.g., for generation).

Parameters

**kwargs – The keyword arguments intended for __call__.

Returns

The prepared keyword arguments.

Return type

dict

prepare_inputs_for_generation(input_ids, max_length: int, pad_token_id: int, starts: int | None = None, past_key_values=None, attention_mask=None, inputs_embeds=None, position_ids=None, pixel_values=None, pixel_values_videos=None, image_grid_thw=None, video_grid_thw=None, **kwargs)

Sets up the initial inputs required for starting autoregressive generation.

This function initializes the Key-Value cache (past_key_values) using init_cache, calculates the initial position_ids based on the input attention_mask (or assumes a contiguous range if no mask is provided), and prepares an extended attention_mask suitable for caching. It ensures inputs are placed on the correct devices/shards.

Parameters

• input_ids (chex.Array) – The initial sequence of token IDs. Shape (batch_size, seq_length).

• max_length (int) – The maximum sequence length that the KV cache should support.

• pad_token_id (int) – The ID used for padding tokens. Used to calculate starts if not provided.

• starts (int | None) – Optional pre-calculated starting positions (number of leading pads). If None, calculated using compute_prefill_length.

• shardings (dict | None) – Optional sharding configuration passed to init_cache.

• attention_mask (tp.Optional[chex.Array]) – An optional mask indicating which tokens should be attended to. Shape (batch_size, seq_length).

• token_type_ids (tp.Optional[chex.Array]) – Optional segment IDs for models that use them.

Returns

A dictionary containing the prepared inputs, typically including:

• ”past_key_values”: The initialized KV cache.

• ”attention_mask”: The extended attention mask for generation.

• ”position_ids”: The calculated initial position IDs.

• ”token_type_ids”: (Optional) Prepared token type IDs.

This dictionary is then passed through prepare_inputs_for_call.

Return type

dict

update_inputs_for_generation(model_outputs, model_kwargs)

Updates the keyword arguments for the next generation step.

Specifically, it takes the past_key_values from the model_outputs of the current step and updates the model_kwargs with them. It also increments the position_ids by one for the next token prediction.

Parameters

• model_outputs – The output object from the model’s forward pass in the previous step (should contain a past_key_values attribute).

• model_kwargs (dict) – The dictionary of keyword arguments used for the model call. This dictionary will be modified in-place or a new one returned.

Returns

The updated model_kwargs dictionary ready for the next generation step.

Return type

dict

class easydel.modules.qwen2_vl.__init__.Qwen2VLModel(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule