| """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" | |
| ) | |