easydel.modules.qwen2.__init__

class easydel.modules.qwen2.__init__.Qwen2Config(vocab_size=151936, hidden_size=4096, intermediate_size=22016, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=32, hidden_act='silu', max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-06, use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=28, attention_dropout=0.0, resid_pdrop: float = 0.0, embd_pdrop: float = 0.0, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, fcm_min_ratio: float = 0.0, fcm_max_ratio: float = 0.0, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, number_rep_kv: int = 1, bits: Optional[int] = None, scan_layers: bool = True, rope_scaling: Optional[Mapping[str, str | float]] = None, **kwargs)

Bases: EasyDeLBaseConfig

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 151936) – Vocabulary size of the Qwen-2 model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

• hidden_size (int, optional, defaults to 4096) – Dimensionality of the encoder layers and the pooler layer.

• intermediate_size (int, optional, defaults to 22016) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

• num_hidden_layers (int, optional, defaults to 32) – Number of hidden layers in the Transformer encoder.

• num_attention_heads (int, optional, defaults to 32) – Number of attention heads for each attention layer in the Transformer encoder.

• num_key_value_heads (int, optional, defaults to 32) – Number of key and value heads for each attention layer in the Transformer encoder.

• hidden_act (str or function, optional, defaults to “silu”) – The non-linear activation function (function or string) to use in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

• max_position_embeddings (int, optional, defaults to 32768) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 2048 or 4096).

• 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-6) – 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 to tie the weights of the input embeddings and the output embeddings.

• rope_theta (float, optional, defaults to 10000.0) – The theta value to use for rotary position embeddings.

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

• sliding_window (int, optional, defaults to 4096) – The sliding window size.

• max_window_layers (int, optional, defaults to 28) – The maximum number of layers to use for the sliding window attention.

• attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

• resid_pdrop (float, optional, defaults to 0.0) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

• embd_pdrop (float, optional, defaults to 0.0) – The dropout ratio for the embeddings.

• gradient_checkpointing (str, optional, defaults to “nothing_saveable”) – The gradient checkpointing configuration.

• fcm_min_ratio (float, optional, defaults to 0.0) – The minimum ratio for Flash Attention.

• fcm_max_ratio (float, optional, defaults to 0.0) – The maximum ratio for Flash Attention.

• use_scan_mlp (bool, optional, defaults to False) – Whether to use the scan implementation for the MLP.

• scan_mlp_chunk_size (int, optional, defaults to 1024) – The chunk size to use when scanning the MLP.

• number_rep_kv (int, optional, defaults to 1) – Number of repetitions for the key and value vectors.

• bits (int, optional) – The number of bits to quantize the model to.

• scan_layers (bool, optional, defaults to True) – Whether to use the scan implementation for the layers.

• rope_scaling (tp.Dict[str, tp.Union[str, float]], optional) – The configuration for rope scaling.

add_jax_args(resid_pdrop: float = 0.0, embd_pdrop: float = 0.0, attention_dropout: float = 0.0, tie_word_embeddings: bool = False, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, fcm_min_ratio: float = 0.0, fcm_max_ratio: float = 0.0, use_scan_mlp: bool = False, scan_mlp_chunk_size: int = 1024, number_rep_kv: int = 1, bits: Optional[int] = None, rope_theta: float = 10000.0, hidden_act: str = 'silu', scan_layers: bool = True, rope_scaling: Optional[Mapping[str, str | float]] = None, **kwargs)

The add_jax_args function adds the following arguments to the Transformer class:

Parameters

• self – Refer to the current object

• resid_pdrop – float: Set the dropout rate for residual connections

• embd_pdrop – float: Set the probability of dropping an embedding

• attention_dropout – float: Set the probability of dropping out the attention layer

• tie_word_embeddings – bool: Tie the word embeddings to the decoder

• gradient_checkpointing – str: Control the amount of memory used by jax

• fcm_min_ratio – float: Control the minimum ratio of the number of chunks to be used in flash-based computation

• fcm_max_ratio – float: Set the maximum ratio of the number of input tokens to output tokens

• use_scan_mlp – bool: Determine whether to use the scan_mlp function or not

• scan_mlp_chunk_size – int: Set the chunk size for scan_mlp

• number_rep_kv – int: Determine how many times the key and value vectors are repeated

• bits – tp.Optional[int]: Determine the number of bits used in the quantization

• rope_theta – float : rope_theta for compute rope

• hidden_act – str : hidden_act for mlp

• scan_layers – bool: Determine whether to use scan layers or not

Return type

The following

get_partition_rules(fully_sharded_data_parallel: bool = True)

Get the partition rules for the model.

Parameters

fully_sharded_data_parallel (bool, optional, defaults to True) – Whether to use fully sharded data parallelism.

Returns

The partition rules.

Return type

tp.Tuple[tp.Tuple[str, PartitionSpec]]

static get_weight_decay_exclusions()

property granted_freq_max_position_embedding: int

property granted_mask_max_position_embedding: int

model_type: str = 'qwen2'

static rng_keys()

class easydel.modules.qwen2.__init__.Qwen2ForCausalLM(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.qwen2.__init__.Qwen2ForSequenceClassification(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule

class easydel.modules.qwen2.__init__.Qwen2Model(*args: Any, **kwargs: Any)

Bases: EasyDeLBaseModule