configuration_mpt.py

"""A HuggingFace-style model configuration."""
from typing import Dict, Optional, Union
from transformers import PretrainedConfig

attn_config_defaults: Dict = {
    "attn_type": "multihead_attention",
    "attn_pdrop": 0.0,
    "attn_impl": "triton",
    "qk_ln": False,
    "clip_qkv": None,
    "softmax_scale": None,
    "prefix_lm": False,
    "attn_uses_sequence_id": False,
    "alibi": False,
    "alibi_bias_max": 8,
}
init_config_defaults: Dict = {
    "name": "kaiming_normal_",
    "fan_mode": "fan_in",
    "init_nonlinearity": "relu",
}


class MPTConfig(PretrainedConfig):
    model_type = "mpt"

    def __init__(
        self,
        d_model: int = 2048,
        n_heads: int = 16,
        n_layers: int = 24,
        expansion_ratio: int = 4,
        max_seq_len: int = 2048,
        vocab_size: int = 50368,
        resid_pdrop: float = 0.0,
        emb_pdrop: float = 0.0,
        learned_pos_emb: bool = True,
        attn_config: Dict = attn_config_defaults,
        init_device: str = "cpu",
        logit_scale: Optional[Union[float, str]] = None,
        no_bias: bool = False,
        verbose: int = 0,
        embedding_fraction: float = 1.0,
        norm_type: str = "low_precision_layernorm",
        use_cache: bool = False,
        init_config: Dict = init_config_defaults,
        **kwargs,
    ):
        """The MPT configuration class.

        Args:
            d_model (int): The size of the embedding dimension of the model.
            n_heads (int): The number of attention heads.
            n_layers (int): The number of layers in the model.
            expansion_ratio (int): The ratio of the up/down scale in the MLP.
            max_seq_len (int): The maximum sequence length of the model.
            vocab_size (int): The size of the vocabulary.
            resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
            emb_pdrop (float): The dropout probability for the embedding layer.
            learned_pos_emb (bool): Whether to use learned positional embeddings
            attn_config (Dict):  A dictionary used to configure the model's attention module:
                attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
                attn_pdrop (float): The dropout probability for the attention layers.
                attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
                qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
                clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
                    this value.
                softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
                    use the default scale of ``1/sqrt(d_keys)``.
                prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
                    extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
                    can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
                attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
                    When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
                    which sub-sequence each token belongs to.
                    Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
                alibi (bool): Whether to use the alibi bias instead of position embeddings.
                alibi_bias_max (int): The maximum value of the alibi bias.
            init_device (str): The device to use for parameter initialization.
            logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
            no_bias (bool): Whether to use bias in all layers.
            verbose (int): The verbosity level. 0 is silent.
            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
            norm_type (str): choose type of norm to use
            multiquery_attention (bool): Whether to use multiquery attention implementation.
            use_cache (bool): Whether or not the model should return the last key/values attentions
            init_config (Dict): A dictionary used to configure the model initialization:
                init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
                    'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
                    'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
                init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
                emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
                emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
                    used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
                init_std (float): The standard deviation of the normal distribution used to initialize the model,
                    if using the baseline_ parameter initialization scheme.
                init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
                fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
                init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
                ---
                See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
        """
        self.d_model = d_model
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.expansion_ratio = expansion_ratio
        self.max_seq_len = max_seq_len
        self.vocab_size = vocab_size
        self.resid_pdrop = resid_pdrop
        self.emb_pdrop = emb_pdrop
        self.learned_pos_emb = learned_pos_emb
        self.attn_config = attn_config
        self.init_device = init_device
        self.logit_scale = logit_scale
        self.no_bias = no_bias
        self.verbose = verbose
        self.embedding_fraction = embedding_fraction
        self.norm_type = norm_type
        self.use_cache = use_cache
        self.init_config = init_config
        if "name" in kwargs:
            del kwargs["name"]
        if "loss_fn" in kwargs:
            del kwargs["loss_fn"]
        super().__init__(**kwargs)
        self._validate_config()

    def _set_config_defaults(self, config, config_defaults):
        for k, v in config_defaults.items():
            if k not in config:
                config[k] = v
        return config

    def _validate_config(self):
        self.attn_config = self._set_config_defaults(
            self.attn_config, attn_config_defaults
        )
        self.init_config = self._set_config_defaults(
            self.init_config, init_config_defaults
        )
        if self.d_model % self.n_heads != 0:
            raise ValueError("d_model must be divisible by n_heads")
        if any(
            (
                prob < 0 or prob > 1
                for prob in [
                    self.attn_config["attn_pdrop"],
                    self.resid_pdrop,
                    self.emb_pdrop,
                ]
            )
        ):
            raise ValueError(
                "self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1"
            )
        if self.attn_config["attn_impl"] not in ["torch", "flash", "triton"]:
            raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
        if self.attn_config["prefix_lm"] and self.attn_config["attn_impl"] not in [
            "torch",
            "triton",
        ]:
            raise NotImplementedError(
                "prefix_lm only implemented with torch and triton attention."
            )
        if self.attn_config["alibi"] and self.attn_config["attn_impl"] not in [
            "torch",
            "triton",
        ]:
            raise NotImplementedError(
                "alibi only implemented with torch and triton attention."
            )
        if self.attn_config["attn_uses_sequence_id"] and self.attn_config[
            "attn_impl"
        ] not in ["torch", "triton"]:
            raise NotImplementedError(
                "attn_uses_sequence_id only implemented with torch and triton attention."
            )
        if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
            raise ValueError(
                "model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!"
            )
        if isinstance(self.logit_scale, str) and self.logit_scale != "inv_sqrt_d_model":
            raise ValueError(
                f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'."
            )
        if self.init_config.get("name", None) is None:
            raise ValueError(
                f"self.init_config={self.init_config!r} 'name' needs to be set."
            )
        if not self.learned_pos_emb and (not self.attn_config["alibi"]):
            raise ValueError(
                f"Positional information must be provided to the model using either learned_pos_emb or alibi."
            )