| from typing import Optional |
| from transformers.modeling_rope_utils import rope_config_validation |
| from transformers.configuration_utils import layer_type_validation |
| from transformers.utils import logging |
| from transformers import PretrainedConfig |
|
|
| import transformers.configuration_utils as configuration_util |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class PMNetConfig(PretrainedConfig): |
|
|
| model_type = "PMNet" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| base_model_tp_plan = { |
| "layers.*.self_attn.q_proj": "colwise", |
| "layers.*.self_attn.k_proj": "colwise", |
| "layers.*.self_attn.v_proj": "colwise", |
| "layers.*.self_attn.o_proj": "rowwise", |
| "layers.*.mlp.gate_proj": "colwise", |
| "layers.*.mlp.up_proj": "colwise", |
| "layers.*.mlp.down_proj": "rowwise", |
| } |
| base_model_pp_plan = { |
| "embed_tokens": (["input_ids"], ["inputs_embeds"]), |
| "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), |
| "norm": (["hidden_states"], ["hidden_states"]), |
| } |
|
|
| def __init__( |
| self, |
| vocab_size: Optional[int] = 151936, |
| hidden_size: Optional[int] = 4096, |
| intermediate_size: Optional[int] = 22016, |
| num_hidden_layers: Optional[int] = 32, |
| num_attention_heads: Optional[int] = 32, |
| num_key_value_heads: Optional[int] = 32, |
| head_dim: Optional[int] = 128, |
| memory_size: Optional[int] = 64, |
| num_memory: Optional[int] = 32, |
| num_memory_read_heads: Optional[int] = 8, |
| memory_write_period: Optional[int] = 4, |
| hidden_act: Optional[str] = "silu", |
| max_position_embeddings: Optional[int] = 32768, |
| initializer_range: Optional[float] = 0.02, |
| rms_norm_eps: Optional[int] = 1e-6, |
| use_cache: Optional[bool] = True, |
| tie_word_embeddings: Optional[bool] = False, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| attention_bias: Optional[bool] = False, |
| use_sliding_window: Optional[bool] = False, |
| sliding_window: Optional[int] = 4096, |
| max_window_layers: Optional[int] = 28, |
| layer_types: Optional[list[str]] = None, |
| attention_dropout: Optional[float] = 0.0, |
| memory_cumsum: bool = True, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| self.max_position_embeddings = max_position_embeddings |
| 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 self.use_sliding_window else None |
| self.max_window_layers = max_window_layers |
|
|
| self.memory_size = memory_size |
| self.num_memory = num_memory |
| self.num_memory_read_heads = num_memory_read_heads |
| self.memory_write_period = memory_write_period |
|
|
| |
| if num_key_value_heads is None: |
| num_key_value_heads = num_attention_heads |
|
|
| self.num_key_value_heads = num_key_value_heads |
| self.head_dim = head_dim |
| 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_bias = attention_bias |
| self.attention_dropout = attention_dropout |
| self.memory_cumsum = memory_cumsum |
|
|
| if self.rope_scaling is not None and "type" in self.rope_scaling: |
| self.rope_scaling["rope_type"] = self.rope_scaling["type"] |
| rope_config_validation(self) |
|
|
| self.layer_types = layer_types |
| if self.layer_types is None: |
| self.layer_types = [ |
| ( |
| "sliding_attention" |
| if self.sliding_window is not None and i >= self.max_window_layers |
| else "full_attention" |
| ) |
| for i in range(self.num_hidden_layers) |
| ] |
| layer_type_validation(self.layer_types, self.num_hidden_layers) |
|
|
| super().__init__( |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
|
|
| __all__ = ["PMNetConfig"] |
|
|
