easydel.modules.qwen2_moe.configuration_qwen2_moe

easydel.modules.qwen2_moe.configuration_qwen2_moe#

class easydel.modules.qwen2_moe.configuration_qwen2_moe.ExpertTensorParallel(axes: Sequence[str | None], mode: Union[Literal['__autoregressive__', '__prefill__', '__train__', '__insert__'], int])[source]#

Bases: DynamicShardingAxes

Expert Tensor Parallelism (EPxTP) sharding axes.

axes: ClassVar = ['__TENSOR_PARALLEL__', '_', '_']#
mode: ClassVar = '__train__'#
class easydel.modules.qwen2_moe.configuration_qwen2_moe.Qwen2MoeConfig(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_norm_eps=1e-06, use_cache=True, tie_word_embeddings=False, qkv_bias=False, rope_theta=10000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=28, attention_dropout=0.0, decoder_sparse_step=1, moe_intermediate_size=1408, shared_expert_intermediate_size=5632, num_experts_per_tok=4, num_experts=60, norm_topk_prob=False, output_router_logits=False, router_aux_loss_coef=0.001, mlp_only_layers=None, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, bits: int | None = None, layer_types: list[str] | None = None, **kwargs)[source]#

Bases: EasyDeLBaseConfig

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

Parameters

  • vocab_size (int, optional, defaults to 151936) – Vocabulary size of the Qwen-2 MoE 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 2048) – Dimensionality of the encoder layers and the pooler layer.

  • intermediate_size (int, optional, defaults to 5632) – Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.

  • num_hidden_layers (int, optional, defaults to 24) – Number of hidden layers in the Transformer encoder.

  • num_attention_heads (int, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer encoder.

  • num_key_value_heads (int, optional, defaults to 16) – 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.

  • decoder_sparse_step (int, optional, defaults to 1) – The sparse step for the decoder.

  • moe_intermediate_size (int, optional, defaults to 1408) – The intermediate size of the MoE layer.

  • shared_expert_intermediate_size (int, optional, defaults to 5632) – The intermediate size of the shared expert.

  • num_experts_per_tok (int, optional, defaults to 4) – The number of experts per token.

  • num_experts (int, optional, defaults to 60) – The number of experts.

  • norm_topk_prob (bool, optional, defaults to False) – Whether to normalize the top-k probabilities.

  • output_router_logits (bool, optional, defaults to False) – Whether to output the router logits.

  • router_aux_loss_coef (float, optional, defaults to 0.001) – The coefficient for the router auxiliary loss.

  • mlp_only_layers (list of int, optional) – The layers that should only contain an MLP.

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

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

get_mask_details() → dict[int, easydel.infra.utils.AttnMaskDetail][source]#

Retrieve attention mask details for each layer in the model.

This method generates a dictionary mapping layer indices to their corresponding attention mask details. If a sliding window is defined, each layer is assigned a sliding window attention mask with the specified size.

Returns

A dictionary where keys are layer indices (int) and values are AttnMaskDetail objects specifying the attention mask type and size for each layer.

Return type

dict[int, AttnMaskDetail]

Notes

  • If self.sliding_window is None, an empty dictionary is returned.

  • The method iterates over self.num_hidden_layers to assign mask details for each layer.

  • The attention mask type is set to AttnMaskType.SLIDING when a sliding window is defined.

get_partition_rules(*args, **kwargs)[source]#

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

property granted_freq_max_position_embedding: int#

Returns the maximum position embedding size specifically for frequency-based position embeddings.

If freq_max_position_embeddings is set, it returns that value. Otherwise, it falls back to max_position_embeddings.

Returns

The granted maximum position embedding size for frequency encoding.

Return type

int

property granted_mask_max_position_embedding: int#

Returns the maximum position embedding size specifically for mask-based position embeddings.

If mask_max_position_embeddings is set, it returns that value. Otherwise, it falls back to max_position_embeddings.

Returns

The granted maximum position embedding size for mask encoding.

Return type

int

model_type: str = 'qwen2_moe'#
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