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import warnings |
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from transformers.configuration_utils import PretrainedConfig |
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class GatedDeltaProductConfig(PretrainedConfig): |
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model_type = "gated_deltaproduct" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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attn_mode: str = "chunk", |
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conv_size: int = 4, |
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head_dim: int = 256, |
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num_heads: int = 6, |
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hidden_size: int = 2048, |
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expand_v: float = 2.0, |
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use_gate: bool = True, |
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use_short_conv: bool = True, |
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max_position_embeddings: int = 2048, |
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hidden_ratio: int | None = 4, |
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intermediate_size: int | None = None, |
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hidden_act: str = "swish", |
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num_hidden_layers: int = 21, |
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norm_eps: float = 1e-6, |
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attn: dict | None = None, |
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use_cache: bool = True, |
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pad_token_id: int = None, |
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bos_token_id: int = 1, |
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eos_token_id: int = 2, |
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tie_word_embeddings: bool = False, |
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initializer_range: float = 0.02, |
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fuse_norm: bool = True, |
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fuse_swiglu: bool = True, |
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fuse_cross_entropy: bool = True, |
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fuse_linear_cross_entropy: bool = False, |
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use_l2warp: bool = False, |
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vocab_size: int = 32000, |
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use_forget_gate: bool = False, |
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allow_neg_eigval: bool = False, |
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num_householder: int = 1, |
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**kwargs, |
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): |
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self.attn_mode = attn_mode |
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self.conv_size = conv_size |
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self.head_dim = head_dim |
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self.num_heads = num_heads |
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self.hidden_size = hidden_size |
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self.expand_v = expand_v |
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self.use_gate = use_gate |
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self.use_short_conv = use_short_conv |
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self.max_position_embeddings = max_position_embeddings |
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self.hidden_ratio = hidden_ratio |
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self.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.num_hidden_layers = num_hidden_layers |
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self.norm_eps = norm_eps |
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self.attn = attn |
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self.use_cache = use_cache |
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self.initializer_range = initializer_range |
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self.fuse_norm = fuse_norm |
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self.fuse_swiglu = fuse_swiglu |
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self.fuse_cross_entropy = fuse_cross_entropy |
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self.fuse_linear_cross_entropy = fuse_linear_cross_entropy |
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self.use_l2warp = use_l2warp |
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self.vocab_size = vocab_size |
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if fuse_cross_entropy and fuse_linear_cross_entropy: |
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raise ValueError( |
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"`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.", |
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) |
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if fuse_linear_cross_entropy: |
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warnings.warn( |
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"`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency " |
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"at the potential cost of reduced precision. " |
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"If you observe issues like loss divergence, consider disabling this setting.", |
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stacklevel=2, |
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) |
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self.allow_neg_eigval = allow_neg_eigval |
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self.num_householder = num_householder |
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self.use_forget_gate = use_forget_gate |
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if attn is not None: |
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if not isinstance(attn, dict): |
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raise ValueError("attn must be a dictionary") |
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if "layers" not in attn: |
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raise ValueError("Layer indices must be provided to initialize hybrid attention layers") |
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if "num_heads" not in attn: |
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raise ValueError("Number of heads must be provided to initialize hybrid attention layers") |
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attn["num_kv_heads"] = attn.get("num_kv_heads", attn["num_heads"]) |
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attn["qkv_bias"] = attn.get("qkv_bias", False) |
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attn["window_size"] = attn.get("window_size", None) |
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attn["rope_theta"] = attn.get("rope_theta", 10000.0) |
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super().__init__( |
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pad_token_id=pad_token_id, |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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