configuration_mpt.py

"""A HuggingFace-style model configuration."""

import warnings
from typing import Any, Dict, Optional, Union
from transformers import PretrainedConfig
from .attention import check_alibi_support, is_flash_v1_installed, is_flash_v2_installed
from .blocks import attn_config_defaults
from .fc import FC_CLASS_REGISTRY
from .norm import LPLayerNorm
from .ffn import FFN_CLASS_REGISTRY
from .warnings import VersionedDeprecationWarning

ffn_config_defaults: Dict = {"ffn_type": "mptmlp"}
init_config_defaults: Dict = {
    "name": "kaiming_normal_",
    "fan_mode": "fan_in",
    "init_nonlinearity": "relu",
    "init_div_is_residual": True,
    "emb_init_std": None,
    "emb_init_uniform_lim": None,
    "init_std": None,
    "init_gain": 0.0,
}


class MPTConfig(PretrainedConfig):
    model_type = "mpt"

    def __init__(
        self,
        d_model: int = 2048,
        n_heads: int = 16,
        n_layers: int = 24,
        expansion_ratio: Union[int, float] = 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,
        ffn_config: Dict = ffn_config_defaults,
        init_device: str = "cpu",
        logit_scale: Optional[Union[float, str]] = None,
        no_bias: bool = False,
        embedding_fraction: float = 1.0,
        norm_type: str = "low_precision_layernorm",
        use_cache: bool = False,
        init_config: Dict = init_config_defaults,
        fc_type: str = "torch",
        tie_word_embeddings: bool = True,
        use_pad_tok_in_ffn: bool = True,
        **kwargs: Any,
    ):
        """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 (Union[int, float]): The ratio of the up/down scale in the ffn.
            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, grouped_query_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.
                qk_gn (bool): Whether to apply group 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.
                sliding_window_size (int): Window size for sliding window local attention. Defaults to -1, which means no sliding window. Query at position i will only attend to keys between [i + seqlen_k - seqlen_q - window_size, i + seqlen_k - seqlen_q + window_size] inclusive. Only works for flash attention v2.3.0 or higher.
                alibi (bool): Whether to use the alibi bias instead of position embeddings.
                alibi_bias_max (int): The maximum value of the alibi bias.
                rope (bool): Whether to use rotary positional embeddings.
                rope_theta (int): The base frequency for rope.
                rope_impl (str): The implementation of rope to use. One of 'hf' (to use the implementation from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) or 'dail' (to use the implementation from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py).
                rope_dail_config (Dict): The configuration for the dail implementation of rope.
                    type (str): The type of rotary position embedding to use. Options: 'original' (for https://arxiv.org/pdf/2104.09864.pdf), 'xpos' (for https://arxiv.org/pdf/2212.10554.pdf).
                    pos_idx_in_fp32 (bool): If True, the position indices [0, ..., seqlen - 1] are in fp32, otherwise they might be in lower precision. A consequence could be, for example, that bf16 rounds position 1995 to 2000, which leads to them having the same positional embedding.
                    xpos_scale_base (float): The scale base for XPos (if using XPos).
                rope_hf_config (Dict): A dictionary used to configure rope's scaling behavior (when scaling beyond the training length).
                    type (str): Can be one of 'no_scaling', 'linear', or 'dynamic'. 'no_scaling' uses the default implementation for rotary embeddings, 'linear' uses linear scaling as proposed by the Reddit user /u/kaiokendev, and 'dynamic' uses Dynamic NTK scaling as proposed by the Reddit users /u/bloc97 and /u/emozilla.
                    factor (float): Scaling factor to use if using 'linear' or 'dynamic' as rope_scaling.type.
                kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
            ffn_config (Dict): A dictionary used to configure the model's ffn module:
                ffn_type (str): type of ffn to use. Options: mptmlp, mptglu, te_ln_mlp
            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.
            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
            norm_type (str): choose type of norm to use
            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
            fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs.
            tie_word_embeddings (bool): Whether to tie the input embedding and output layers.
            use_pad_tok_in_ffn (bool): Whether to forward the pad token in the feedforward networks.
        """
        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.ffn_config = ffn_config
        self.init_device = init_device
        self.logit_scale = logit_scale
        self.no_bias = no_bias
        self.embedding_fraction = embedding_fraction
        self.norm_type = norm_type
        self.use_cache = use_cache
        self.init_config = init_config
        self.fc_type = fc_type
        self.use_pad_tok_in_ffn = use_pad_tok_in_ffn
        if "name" in kwargs:
            del kwargs["name"]
        if "loss_fn" in kwargs:
            del kwargs["loss_fn"]
        if self.attn_config.get("alibi", False) or self.attn_config.get("rope", False):
            self.learned_pos_emb = False
            warnings.warn(
                f"alibi or rope is turned on, setting `learned_pos_emb` to `False.`"
            )
        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
        self._validate_config()

    def _set_config_defaults(
        self, config: Dict[str, Any], config_defaults: Dict[str, Any]
    ) -> Dict[str, Any]:
        for k, v in config_defaults.items():
            if k not in config:
                config[k] = v
            elif isinstance(v, dict):
                config[k] = self._set_config_defaults(
                    config[k] if config[k] is not None else {}, v
                )
        return config

    def _validate_config(self) -> None:
        self.attn_config = self._set_config_defaults(
            self.attn_config, attn_config_defaults
        )
        self.ffn_config = self._set_config_defaults(
            self.ffn_config, ffn_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["attn_impl"] == "flash" and is_flash_v1_installed():
            warnings.warn(
                VersionedDeprecationWarning(
                    'Support for Flash Attention v1 is deprecated. Please upgrade to Flash Attention v2.4.2. To install Flash Attention v2.4.2, please run `pip install -e ".[gpu-flash2]"` from the root directory of the llm-foundry repository.',
                    remove_version="0.6.0",
                )
            )
        if self.attn_config["attn_impl"] == "triton" and (
            not self.attn_config["prefix_lm"]
        ):
            warnings.warn(
                UserWarning(
                    'If not using a Prefix Language Model, we recommend setting "attn_impl" to "flash" instead of "triton".'
                )
            )
        if self.attn_config["alibi"] and (
            not check_alibi_support(self.attn_config["attn_impl"])
        ):
            raise NotImplementedError(
                "alibi only implemented with torch, triton, and flash (v2.4.2 or higher) attention."
            )
        if self.attn_config["attn_uses_sequence_id"] and (
            not (
                self.attn_config["attn_impl"] in ["torch", "triton"]
                or (
                    self.attn_config["attn_impl"] == "flash"
                    and is_flash_v2_installed(v2_version="v2.1.2")
                )
            )
        ):
            raise NotImplementedError(
                "attn_uses_sequence_id only implemented with torch, triton, and flash (v2.1.2 or higher) attention."
            )
        if self.attn_config["rope"] and self.attn_config["rope_impl"] not in [
            "dail",
            "hf",
        ]:
            raise ValueError(
                'If rope is being used then rope_impl should be either "dail", or "hf".'
            )
        if (
            self.attn_config["rope"]
            and self.attn_config["rope_impl"] == "hf"
            and (
                self.attn_config["rope_hf_config"]["type"]
                not in ["no_scaling", "linear", "dynamic"]
            )
        ):
            raise ValueError(
                'If using hf implementation of rope, the type should be one of "no_scaling", "linear" or "dynamic".'
            )
        if self.attn_config["rope"] and self.attn_config["rope_impl"] == "dail":
            if self.attn_config["rope_dail_config"]["type"] not in ["original", "xpos"]:
                raise ValueError(
                    'If using the dail implementation of rope, the type should be one of "original" or "xpos".'
                )
            if not is_flash_v2_installed(v2_version="2.0.1"):
                raise ImportError(
                    "If using the dail implementation of rope, the flash_attn library v2.0.1 or higher must be installed. Please check the instructions at https://github.com/mosaicml/llm-foundry/blob/main/TUTORIAL.md#what-kinds-of-positional-embeddings-does-llm-foundry-support"
                )
        if self.attn_config["sliding_window_size"] != -1 and (
            not (
                self.attn_config["attn_impl"] == "flash"
                and is_flash_v2_installed(v2_version="v2.3.0")
            )
        ):
            raise NotImplementedError(
                "sliding window only implemented with flash attention v2.3.0 or higher."
            )
        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
            or self.attn_config["alibi"]
            or self.attn_config["rope"]
        ):
            warnings.warn(
                f"Positional information not being provided to the model using either learned_pos_emb or alibi or rope."
            )
        if self.fc_type == "te" or self.ffn_config["ffn_type"] == "te_ln_mlp":
            try:
                import transformer_engine.pytorch as te

                del te
            except:
                raise ImportError(
                    "TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. "
                    + "The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n"
                    + "pip install flash-attn==1.0.6 --no-build-isolation \n"
                    + "pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156"
                )
        if self.ffn_config["ffn_type"] == "mptgeglu":
            raise ValueError(
                'API CHANGE: `ffn_type=="mptgeglu"` changed to `ffn_type=="mptglu"`. '
                + "See [#829](https://github.com/mosaicml/llm-foundry/pull/829) for details."
            )
        elif self.ffn_config["ffn_type"] in ["mptmlp", "mptglu"]:
            self.ffn_config["fc_type"] = self.fc_type
        elif self.ffn_config["ffn_type"] == "te_ln_mlp":
            self.ffn_config["bias"] = not self.no_bias
            if "ffn_act_fn" in self.ffn_config.keys():
                raise ValueError(
                    f"Transformer Engine block does not support custom activation functions."
                )
        if not self.use_pad_tok_in_ffn:
            try:
                from flash_attn.bert_padding import unpad_input, pad_input
            except:
                raise ImportError(
                    "In order to set `use_pad_tok_in_ffn=False`, please install flash-attn==1.0.9 or flash-attn==2.3.6"
                )